{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.4 Deep Taylor Decomposition Part 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow Walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import Dependencies\n",
    "\n",
    "I made a custom `Taylor` class for Deep Taylor Decomposition. If you are interested in the details, check out `models_3_2.py` in the models directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from tensorflow.python.ops import nn_ops, gen_nn_ops\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from models.models_2_4 import MNIST_CNN, Taylor\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "images = mnist.train.images\n",
    "labels = mnist.train.labels\n",
    "\n",
    "logdir = './tf_logs/2_4_DTD/'\n",
    "ckptdir = logdir + 'model'\n",
    "\n",
    "if not os.path.exists(logdir):\n",
    "    os.mkdir(logdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Building Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('Classifier'):\n",
    "\n",
    "    # Initialize neural network\n",
    "    DNN = MNIST_CNN('CNN')\n",
    "\n",
    "    # Setup training process\n",
    "    X = tf.placeholder(tf.float32, [None, 784], name='X')\n",
    "    Y = tf.placeholder(tf.float32, [None, 10], name='Y')\n",
    "\n",
    "    activations, logits = DNN(X)\n",
    "    \n",
    "    tf.add_to_collection('DTD', X)\n",
    "    \n",
    "    for activation in activations:\n",
    "        tf.add_to_collection('DTD', activation)\n",
    "\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))\n",
    "\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost, var_list=DNN.vars)\n",
    "\n",
    "    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "cost_summary = tf.summary.scalar('Cost', cost)\n",
    "accuray_summary = tf.summary.scalar('Accuracy', accuracy)\n",
    "summary = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Training Network\n",
    "\n",
    "This is the step where the DNN is trained to classify the 10 digits of the MNIST images. Summaries are written into the logdir and you can visualize the statistics using tensorboard by typing this command: `tensorboard --lodir=./tf_logs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 0.155111813 accuracy = 0.951527280\n",
      "Epoch: 0002 cost = 0.044594308 accuracy = 0.985636373\n",
      "Epoch: 0003 cost = 0.029799474 accuracy = 0.990345463\n",
      "Epoch: 0004 cost = 0.021161627 accuracy = 0.992818188\n",
      "Epoch: 0005 cost = 0.017527232 accuracy = 0.994145460\n",
      "Epoch: 0006 cost = 0.014087625 accuracy = 0.995490913\n",
      "Epoch: 0007 cost = 0.013123321 accuracy = 0.995472732\n",
      "Epoch: 0008 cost = 0.009770884 accuracy = 0.996800003\n",
      "Epoch: 0009 cost = 0.009048406 accuracy = 0.996981821\n",
      "Epoch: 0010 cost = 0.009553186 accuracy = 0.996981821\n",
      "Epoch: 0011 cost = 0.007268998 accuracy = 0.997509093\n",
      "Epoch: 0012 cost = 0.007693169 accuracy = 0.997618184\n",
      "Epoch: 0013 cost = 0.005034720 accuracy = 0.998527274\n",
      "Epoch: 0014 cost = 0.006306725 accuracy = 0.997745457\n",
      "Epoch: 0015 cost = 0.004300387 accuracy = 0.998581820\n",
      "Accuracy: 0.9917\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n",
    "\n",
    "# Hyper parameters\n",
    "training_epochs = 15\n",
    "batch_size = 100\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    total_batch = int(mnist.train.num_examples / batch_size)\n",
    "    avg_cost = 0\n",
    "    avg_acc = 0\n",
    "    \n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        _, c, a, summary_str = sess.run([optimizer, cost, accuracy, summary], feed_dict={X: batch_xs, Y: batch_ys})\n",
    "        avg_cost += c / total_batch\n",
    "        avg_acc += a / total_batch\n",
    "        \n",
    "        file_writer.add_summary(summary_str, epoch * total_batch + i)\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost), 'accuracy =', '{:.9f}'.format(avg_acc))\n",
    "    \n",
    "    saver.save(sess, ckptdir)\n",
    "\n",
    "print('Accuracy:', sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Restoring Subgraph\n",
    "\n",
    "Here we first rebuild the DNN graph from metagraph, restore DNN parameters from the checkpoint and then gather the necessary weight and biases for Deep Taylor Decomposition using the `tf.get_collection()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./tf_logs/3_2_DTD/model\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "new_saver = tf.train.import_meta_graph(ckptdir + '.meta')\n",
    "new_saver.restore(sess, tf.train.latest_checkpoint(logdir))\n",
    "\n",
    "weights = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='CNN')\n",
    "activations = tf.get_collection('DTD')\n",
    "X = activations[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Attaching Subgraph for Calculating Relevance Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv_ksize = [1, 3, 3, 1]\n",
    "pool_ksize = [1, 2, 2, 1]\n",
    "conv_strides = [1, 1, 1, 1]\n",
    "pool_strides = [1, 2, 2, 1]\n",
    "\n",
    "weights.reverse()\n",
    "activations.reverse()\n",
    "\n",
    "taylor = Taylor(activations, weights, conv_ksize, pool_ksize, conv_strides, pool_strides, 'Taylor')\n",
    "\n",
    "Rs = []\n",
    "for i in range(10):\n",
    "    Rs.append(taylor(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Calculating Relevance Scores $R(x_i)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_imgs = []\n",
    "for i in range(10):\n",
    "    sample_imgs.append(images[np.argmax(labels, axis=1) == i][3])\n",
    "\n",
    "imgs = []\n",
    "for i in range(10):\n",
    "    imgs.append(sess.run(Rs[i], feed_dict={X: sample_imgs[i][None,:]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Displaying Images\n",
    "\n",
    "The relevance scores are visualized as heat maps. You can see which features/data points influenced the DNN most its decision making."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+DjhI0n5AACuBj43hNZqVQ32jANVKOzVS1mk68Kthx06vPDCf3vJaYEnNqzQr\nAedHsxZo0gh5p6vZIY+IojHkc8fgWszKrb5RgLaUdpK0HfAD4NMR8USrz2/WiZwfzVrAI+Rm1nKN\njQKMpKzTqmFlnZLHSnoRWWf8exHxw4au0MzMbKRKMELuQmpmnaLxJ8kbKevUDyzMq7DsBcwCbs7n\nl58L3BUR32zsBs3MzEaoCVVWuoFHyM06RYPz5PI54UNlncYD5w2VdQIGIqKfrHN9Uf7Q5iNknXby\n/S4ne1hzI3BsRGySdCDwQeAOSbflp/JS4GZm1hqeQ94BUhVNHkrEd6nSVuo/5lbFajLJX7iSlVwO\nScS/l4gPJuK3F4d/f2VxvFq36H8Xh2N9cfyq4jBvOzaxIVGJhgWJ+BeKw/c+Wxx/xaREOwBvS21I\n/XfoAk2YJ9dgWadTgFOGxW7Kr8zMrImeTsQ3JuKJxD85sXu1J122nZrYkKqmkromawnPITezlvMk\nMjMzsy2VIDe6Q27WKcaRrrFvZmZWRiXJje6Qm3WSEowCmJmZjUgJcqM75GadQpRiFMDMzKxuJcmN\nJfidw6xLiOz/yGovMzOzMmlSbpQ0X9IKSYOStirRkZf9vSzfviRfnXpo2wl5fIWkQ/LYKyXdVvF6\nQtKn820nSVpdsS1ZimJIZ4+QvzQRfzIR3zCKti4uDl+f2H3+KYkN0/6mOP6HxP7rEvFfFIc3JYq1\nLE80A/DqxFPJjyX2/1+phm5KxL+UiJ9eHP7LRDWVixP/M0VifwC9JbVlv/RBna4kT5KbmcGURHyb\nRHxlcTj1M7NK/khXTRlp3FqiCblR0njgLGAesApYKqk/Iiq7UUcDj0bETEkLgdOAIyTNJisRvA+w\nO3CtpL0jYgV5pyNvfzVQWRLv9Ij4Rr3X6DE3s04x9LVctZeZmVmZNCc3zgUGI+K+iHgOuJStizQv\nAC7I318BHJwvjrcAuDQino2I35LVrZ477NiDgXsj4ncju7kXuENu1kk8ZcXMzGxLtXPjVEkDFa9j\nhrUwHbi/4vOqPFa4T0RsBB4n+yqnnmMXsvXqLMdJ+rWk8yTtXOsWO3vKilmZlOTBFTMzs7rVlxvX\nR8Scsb+YrUmaCLwLOKEi/B3g/wci//OfgL+q1o7H3Mw6hR/qNDMz21JzcuNqYI+KzzPyWOE+kiYA\nOwIP13HsocCtEbF5OdmIeCgiNkXE88C/sPUUl604xZt1iqEHV6q9zMzMyqQ5uXEpMEvSXvmI9kKg\nf9g+/cCPZREjAAAgAElEQVRR+fvDgesjIvL4wrwKy17ALODmiuOOZNh0FUm7VXx8N3BnrQvs7Ckr\n9yfiA4n49lXa2jERf3dxeP61xfG/uKo4/sNTE+3vnoi/IxF/c3F4fOLeXn1Foh1IVpbZeZeRnZvZ\nifiVxeEf/rI4/rVEM0c8Xxz/+8T+AH+056GJLakn9LvE+HZfgJlZK8wY2e4b/604PuHFxfGVz6Tb\nmvbfxfG7E/v/8XWJDX+UiHd216orNZgbI2KjpOOAq/PWzouIZZJOBgYioh84F7hI0iDwCFmnnXy/\ny8kK220Ejo2ITQCSJpNVbvnYsFN+XdJ+ZFNWVhZs34r/1Zh1Cpc9NDMz21KTcmNELAYWD4udWPF+\nA/DexLGnAFsVvo6Ipymo4RkRHxzp9blDbtYphEfIzczMKpUkN7pDbtYpPEJuZma2pZLkRnfIzTpF\nSUYBzMzM6laS3OgOuVmnKMkogJmZWd1Kkhs7u0P+WCL+QHE4/ifdlFL/MS9MxBMVXn74msT+qWud\nmYj/IhFPVWXZKRE/MRGHZJWV1N8fDybii4rDlyUq0SxNNPOnifhpiXhfqpoOAGdU29i9SjAKYGYG\nUxPxRJIdnyjrxeuLw7ffkD71Hon46Yn433+6OL5n6h4OScRT+1tNJciNNeuQS9pD0g2SlktaJulT\neXwXSddIuif/s+ayoGZWheuQm3UN50azFilJbqxnYaCNwOciYjbwBuBYSbOB44HrImIWcF3+2cxG\na2ieXLWXmXUK50azVihJbqzZIY+INRFxa/7+SeAuYDqwALgg3+0C4LCxukizUijJKIBZL3BuNGuR\nkuTGekbIN5PUB7wWWALsGhFr8k0PArsmjjlG0oCkgXXr1jVwqWYlMK7Gy8w6jnOj2RgrQW6s+zYk\nbQf8APh0RDxRuS0igmx50K1ExKKImBMRc6ZNm9bQxZr1tHHAxBovM+sozo1mY6wkubGuKiuSXkT2\nA+d7EfHDPPyQpN0iYo2k3YC1Tb+6yYn4huJwojAKAE/eXBzfJ/XQ89OJ+N2JeOqp7a0WVM3NScRT\nP5dTX8n8LhEH+O9E/CfF4d8PFse3TZz7iMTf3RF/KI7f9nhxfL8riuPsf2ZiA6TL13S5HvlN36wM\n2pYbe8I2ifi+xeFU7n3VkuL44uIwkM6zqQpkqWpsX05V+zogEXeVlVErQW6sp8qKgHOBuyLimxWb\n+oGj8vdHAT9q/uWZlYlAk6q/zKwjODeatUpzcqOk+ZJWSBqUtNXD1pImSbos374kn4o2tO2EPL5C\n0iEV8ZWS7pB0m6SBiviIqy3V8zvHAcAHgTfnJ7xN0tuAU4F5ku4B3pJ/NrNRE9mXVtVeZtYhnBvN\nWqLx3ChpPHAWcCgwGzgyr4pU6Wjg0YiYSVaV/rT82NnAQmAfYD5wdt7ekD+PiP0ionLuw4irLdW8\ni4i4iexvo8jBtY43s3qJ9Ne4Q1JzqcyslZwbzVqlKblxLjAYEfcBSLqUrCLS8op9FgAn5e+vAM7M\nvwlbAFwaEc8Cv5U0mLf3yyrnWwAclL+/APg58IVqF1iCWTlm3aIpowBj8ZXceZLWSrqzwRs0MzMb\nobpy49ShqkX565hhjUxny0cNV+Wxwn0iYiPwONmTgNWODeBnkm4Zds66qi1V8nfgZh1jHLVHAdIq\nvpKbR/YDY6mk/oioHAHY/JWcpIVkX8kdMewrud2BayXtHRGbgPOBM0k/2mRmZjZG6sqN64dNGWmV\nAyNitaSXANdIujsibqzcISJCUmG1pUqd3SE/IhF/sDi8519WaeuvEvGPJeLvq9JWkeWJ+PaJ+JOJ\neKpUTKJySdUqK48k4om/120Tjx79Z2JcdNz64vgBry2O7/f3iet55zcSGz6QiPeyhpYcG5Ov5CLi\nxsqRdDOzsfOa4nCqktl1zxTHZ6XP8HAiv0/ZPXHAd1MtpcqoWfM1vBznarb8VzQjjxXts0rSBGBH\n4OFqx0bE0J9rJV1JljdvZBTVljxlxaxjDI0CVHtV/VpurL6SMzMza5O6cmMtS4FZkvaSNJHsG+H+\nYftUVkg6HLg+X0ugH1iYT/nci+zXvZslTZa0PYCkycBbgTsL2qqr2lJnj5CblcrQPLmq2vW1nJmZ\nWRvUlRurioiNko4DriYbbj8vIpZJOhkYiIh+sjKmF+XfED9C1mkn3+9ysm+bNwLHRsQmSbsCV2Zf\nMjMB+H5E/Ed+ylOByyUdTTaXoea8C3fIzTpGPU+SVzUmX8mZmZm1T8O5EYCIWMywJaMi4sSK9xuA\n9yaOPQU4ZVjsPhJzrCLiYUZYbclTVsw6RsNVVpr+lVzDt2RmZtaQcqzR0Rt3YdYTGquyMhZfyQFI\nuoSsnupUSauAv4uIc0d9oWZmZnVrLDd2i87ukE/8THF83unF8VSxDoA3JeK3JuIrE/HJiXiqwmSq\nUkzqifFU9ZXvJOLVJhU8NsL48DWrcm98ZWL/P03EPzsxsWFRIv6eRHy7RLxXNWWeXFO/ksvjRzZ0\nUWZmdXtpcXi7ucXxxxJf5H0tfYYplyc2bErEp6Va2ikR7/3OY2s1nhu7Qe/foVnXaM48OTMzs95R\njtzoDrlZxyjHKICZmVn9ypEbe/8OzbpGOebJmZmZ1a8cudEdcrOOUY5RADMzs/qVIzf2/h2adQ0B\nk9p9EWZmZh2kHLnRHXKzjlGOUQAzM7P6lSM3dvgdfrI4vF+iDt/FF6abeu6/i+MvSuyvRA3A5F/Z\nyuJwPFEcT5U33D4R/3QiPrXa2k6pOVdTE/G3J+ILEvHXJ+KpUlBWXTmeJDczS0uVu03UMXzPVpVa\nM7+/IX2KVAXe6Yn4+FRDqYUYUznWRqccudErdZp1jHKsRmZmZla/5uRGSfMlrZA0KOn4gu2TJF2W\nb18iqa9i2wl5fIWkQ/LYHpJukLRc0jJJn6rY/yRJqyXdlr/eVuv6nOHNOkY5niQ3MzOrX+O5UdJ4\n4CxgHrAKWCqpPyKWV+x2NPBoRMyUtBA4DThC0myyVa33AXYHrpW0N9mq1p+LiFslbQ/cIumaijZP\nj4hqS1ZudZdm1hE8Qm5mZralpuTGucBgRNwXEc8Bl7L1fNwFwAX5+yuAgyUpj18aEc9GxG+BQWBu\nRKyJiFsBIuJJ4C7SE59qcofcrGMMzZOr9jIzMyuTpuTG6cD9FZ9XsXXnefM+EbEReByYUs+x+fSW\n1wJLKsLHSfq1pPMk7VzrAt0hN+sYHiE3MzPbUl25caqkgYrXMS27Omk74AfApyM2V/L4DvAKYD9g\nDfBPtdqpmeEl7QFcCOwKBLAoIr4t6STgr4F1+a5fjIjFI7yPGvoS8Y+NMA5MbPBSNtuQiK8qDuv2\n4vgO/5VoZ2Mi/qZEPPWUN7jaSbfxHHKzbtHe3FhGqapeXygOb1sl/+135chOve17ExtS+deDJ81V\nV25cHxFzqmxfDexR8XlGHivaZ5WkCcCOwMPVjpX0IrLO+Pci4odDO0TEQ0PvJf0L8JNaN1DPv5rC\nSev5thFNWDezWvyD3KxLODeatUzDuXEpMEvSXmSd6YXA+4ft0w8cBfwSOBy4PiJCUj/wfUnfJHuo\ncxZwcz6//Fzgroj4ZmVDknaLiDX5x3cDd9a6wJp3mDe4Jn//pKSGJq2bWco4yrAamVkvcG40a5XG\nc2NEbJR0HHA1WWX58yJimaSTgYGI6CfrXF8kaRB4hKzTTr7f5cBysl/Ej42ITZIOBD4I3CHptvxU\nQ9+IfV3SfmTfnq2k6hSOzIh+5Rg2af0AsgnrHwIGyEYKHi045hjgGIA999xzJKczK5lyrEZm1muc\nG83GUnNyY95RXjwsdmLF+w1A4fykiDgFOGVY7Kb84or2/+BIr6/uhzoLJq3XNWE9IhZFxJyImDNt\n2rSRXp9ZibjKilm3cW40G2vlyI11/cpRNGl9NBPWzawaj5CbdRPnRrNWKEdurKfKSuGk9dFMWO8N\nqd/EZo4w/p4mXIv1FldZMesWzo2ttl0ifsgI43imf9cpR26s51eOAyiYtA4cOdIJ62ZWTTlGAcx6\nhHOjWUuUIzfWU2UlNWnddVXNmmponpyZdTrnRrNWKUdu7P1fOcy6RjlGAczMzOpXjtzY+3do1jXK\nMU/OzMysfuXIje6Qm3UU/y9pZma2pd7Pjb1/h2ZdoxyjAGZmZvUrR250h9ysY4hsRV8zMzPLlCM3\nukNu1jHK8SS5mZlZ/cqRG8e1+wLMbMjQk+TVXjVakOZLWiFpUNLxBdsnSbos375EUl/FthPy+ApJ\nh9TbppmZ2dhpPDdCa/OjpL3yNgbzNifWuj53yM06xtA8uWqvNEnjgbOAQ4HZZAuUzB6229HAoxEx\nEzgdOC0/djawENgHmA+cLWl8nW2amZmNkcZyI7QlP54GnJ639Wjeds27NLOO0PAowFxgMCLui4jn\ngEuBBcP2WQBckL+/Ajg4XwJ8AXBpRDwbEb8FBvP26mnTzMxsjDRlhLxl+TE/5s15G+RtHlbrAls6\nh/yWW25ZL+l3+cepwPpWnr8DlO2eu/V+X9aOk95yy61XS9tOrbHbNpIGKj4viohF+fvpwP0V21YB\nrx92/OZ9ImKjpMeBKXn8V8OOnZ6/r9WmmTXAudH33CW6NTdCa/PjFOCxiNhYsH9SSzvkETFt6L2k\ngYiY08rzt1vZ7rls99uoiJjf7msws9ZzbvQ9W1pZcqOnrJj1jtXAHhWfZ+Sxwn0kTQB2BB6ucmw9\nbZqZmXWyVubHh4Gd8jZS59qKO+RmvWMpMCt/unsi2UMo/cP26QeOyt8fDlwfEZHHF+ZPme8FzAJu\nrrNNMzOzTtay/Jgfc0PeBnmbP6p1ge2sQ76o9i49p2z3XLb7bat8zttxwNVkqyicFxHLJJ0MDERE\nP3AucJGkQeARsh8g5PtdDiwHNgLHRsQmgKI2W31vZiVSxp+bvmcbU23Ij18ALpX0VeB/8rarUtaR\nNzMzMzOzdvCUFTMzMzOzNnKH3MzMzMysjVreIS/LMtySzpO0VtKdFbFdJF0j6Z78z53beY3NJGkP\nSTdIWi5pmaRP5fGevWczs2YqQ350bnRutGIt7ZCXbBnu88mWWK10PHBdRMwCrss/94qNwOciYjbw\nBuDY/L9tL9+zmVlTlCg/no9zo3OjbaXVI+SlWYY7Im4ke0q3UuWyrHUtpdotImJNRNyav38SuIts\nZaqevWczsyYqRX50bnRutGKt7pAXLV1acznRHrJrRKzJ3z8I7NrOixkrkvqA1wJLKMk9m5k1qMz5\nsRR5wrnRqvFDnW2SF47vuZqTkrYDfgB8OiKeqNzWq/dsZmbN0at5wrnRaml1h7zsy3A/JGk3gPzP\ntW2+nqaS9CKyHzjfi4gf5uGevmczsyYpc37s6Tzh3Gj1aHWHvOzLcFcuy1rXUqrdQpLIVqK6KyK+\nWbGpZ+/ZzKyJypwfezZPODdavVq+UqektwHf4oVlRk9p6QW0iKRLgIOAqcBDwN8B/w5cDuwJ/A54\nX0QMf7ilK0k6EPhP4A7g+Tz8RbK5cj15z2ZmzVSG/OjcCDg3WoGWd8jNzMzMzOwFfqjTzMzMzKyN\n3CE3MzMzM2sjd8jNzMzMzNrIHXIzMzMzszZyh9zMzMzMrI3cITczMzMzayN3yM3MzMzM2sgdcjMz\nMzOzNnKH3MzMzMysjdwhNzMzMzNrI3fIzczMzMzayB1yMzMzM7M2coe8w0j6rqSvNHtfMzOzbuXc\naL1OEdHuaygNSSuBXYGNwCZgOXAhsCginm+w7YOAiyNixgiO+VvgKOBlwHrg7Ij4x0auw8zMbCQ6\nMDd+Bvj/gKnAU8BlwN9GxMZGrsWsGo+Qt947I2J7sk7wqcAXgHPbdC0CPgTsDMwHjpO0sE3XYmZm\n5dVJubEf+JOI2AHYF3gN8Mk2XYuVhDvkbRIRj0dEP3AEcJSkfQEknS/pq0P7Sfq8pDWSHpD0UUkh\naWblvpImA1cBu0t6Kn/tXsc1fD0ibo2IjRGxAvgRcMBY3K+ZmVktHZIb742Ix4ZOBTwPzGzyrZpt\nwR3yNouIm4FVwBuHb5M0H/gs8BayHwYHJdp4GjgUeCAitstfD0g6UNJjRccUnEv5NSwb1Y2YmZk1\nSbtzo6T3S3qCbDrna4B/buR+zGpxh7wzPADsUhB/H/CvEbEsIp4BThpJoxFxU0TsVOfuJ5H9e/jX\nkZzDzMxsjLQtN0bE9/MpK3sD3wUeGsk5zEbKHfLOMB14pCC+O3B/xef7C/ZpmKTjyOaSvz0inh2L\nc5iZmY1QW3MjQETcQ/bN8dljdQ4zgAntvoCyk/Q6sh86NxVsXgNUPhm+R5WmRlUuR9JfAccDfxYR\nq0bThpmZWTO1OzcOMwF4RRPaMUvyCHmbSNpB0juAS8lKMt1RsNvlwEckvUrSi4FqdVUfAqZI2nEE\n1/AB4GvAvIi4bwSXb2Zm1nQdkhs/Kukl+fvZwAnAdXXfhNkouEPeej+W9CTZV2xfAr4JfKRox4i4\nCjgDuAEYBH6Vb9pqWklE3A1cAtwn6TFJu0t6o6SnqlzLV4EpwNKKJ9C/O9obMzMzG6VOyo0HAHdI\nehpYnL++OLrbMquPFwbqIpJeBdwJTPICBWZmZs6N1hs8Qt7hJL1b0iRJOwOnAT/2DxwzMysz50br\nNe6Qd76PAWuBe8mWFP54ey/HzMys7Zwbrad4yoqZmZmZWRt5hNzMzMzMrI0aqkOeL1/7bWA8cE5E\nnFpt/6lTp0ZfX18jpzQbcytXrmT9+vVq9XknSDW/r3oero6I+S25IDMbtZHkR+dG6wbOjWNr1B1y\nSeOBs4B5wCqy0nn9EbE8dUxfXx8DAwOjPaVZS8yZM6ct5w1gco19noSprbgWMxu9keZH50brBs6N\nY6uRKStzgcGIuC8iniMr4r+gOZdlVj4iG0qr9jKzruD8aNYkZcmNjXTIp5MV8B+yKo9tQdIxkgYk\nDaxbt66B05n1NgEvqvEys65QMz86N5rVpyy5ccwf6oyIRRExJyLmTJs2baxPZ9bVyjAKYGbOjWYj\nUYbc2MhDnauBPSo+z8hjZjYKQ6MAZtb1nB/NmqQsubGREfKlwCxJe0maCCwE+ptzWWblU5Z5cmYl\n4Pxo1iRlyY2jHiGPiI2SjgOuJvv7OC8iljXtysxKpiyjAGa9zvnRrHnKkhsbmkMeEYsjYu+IeEVE\nnNKsizIrq3E1XmbWHZwfzZqnGblR0nxJKyQNSjq+YPskSZfl25dI6svjUyTdIOkpSWdW7P9iST+V\ndLekZZKqrsVTzz2aWQcYB0ys8TIzMyuTZuTGirUBDgVmA0dKmj1st6OBRyNiJnA6cFoe3wB8Bfib\ngqa/ERF/BLwWOEDSoSO4tS24Q27WQTxCbmZmtqUm5MZ61gZYAFyQv78COFiSIuLpiLiJrGO+WUQ8\nExE35O+fA24le4B7VBqpstKBHquy7bZE/PpE/O5EPPWgfOqvMtXO+kT4+eL4M4lmNiTiAKnStqkn\nIF6ciP/xyxMbPpSIH52Ij/rfaSkIj4KbmZlVqjM3TpVUudztoohYVPG5aG2A1w9rY/M++XMgjwNT\nSHbYKq5R2gl4J/Dt2pdarMc65GbdS3gU3MzMrFKduXF9RMwZ84spIGkCcAlwRkTcN9p23CE36xBl\neZLczMysXk3KjfWsDTC0z6q8k70j8HAdbS8C7omIbzVygR6QM+sgZai1amZmNhJNyI31rA3QDxyV\nvz8cuD4iolqjkr5K1nH/dH2XkeYRcrMO4RFyMzOzLTUjN6bWBpB0MjAQEf3AucBFkgaBR8g67dk1\nSCuBHYCJkg4D3go8AXyJ7GHBWyUBnBkR54zmGt0hN+sQQ6uRmZmZWaZZuTEiFgOLh8VOrHi/AXhv\n4ti+KpfXFD3WIf9alW2XFYf/z/8tjv8i0cztifhLE/ElxeGn/1AcvzXRzNOJeLUiK4l6LQwvvDlk\nj0R88msTzygcclJx/B++nmjpi4n4pxLx7RLx3uQRcjPrfBsT8cFEPFWgIpEceSgRvzMRT3VjqmXH\nVG7ZZoTxfRLxfRPxA0Z4PQblyY091iE3624eITczM9tSGXKjO+RmHWIc5RgFMDMzq1dZcqM75GYd\npAyjAGZmZiNRhtzoDrlZhyjLPDkzM7N6lSU3ukNu1iFcZcXMzGxLZcmNvdUhf+of09suT8R/mYhP\nTsQPT8Rfk4h/PNF8orDIG1P/6p5MxKs8SP5oosxKqvrK5E8mNiTWqfr3U4vjrzj1mcL4qz/55eID\nvp16In1BIt6bmjEKIGk+8G2yn1/nRMSpw7ZPAi4E9if7L3tERKzMt50AHA1sAj4ZEVfn8ZVk/wI3\nARvbtTyxmXWCHxWHf59IjqmFxFOVmndKxB9IxGcm4qnCKADTRnju+xPxRLW0ZCmzg05IbPhEIj4j\nES8Xj5CbWcs1snSupPHAWcA8YBWwVFJ/RCyv2O1o4NGImClpIXAacISk2WSLIOwD7A5cK2nviNiU\nH/fnEZGqX2ZmZjZmyrCsfBnu0awrCJhY41XDXGAwIu6LiOeAS9n6a4YFwAX5+yuAg5UtL7YAuDQi\nno2I35IVFZ7b4C2ZmZk1pAm5sSu4Q27WIUT2P2S1FzBV0kDF65iKJqaz5Zerq/IYRftExEbgcWBK\njWMD+JmkW4adz8zMbEzVmRu7nqesmHWIoVGAGta3YQ73gRGxWtJLgGsk3R0RN7b4GszMrITqzI1d\nr1d+sTDrCQ2OAqwG9qj4PCOPFe4jaQKwI9nDncljI2Loz7XAlXgqi5mZtVAZRsh75T7Mut7Qk+TV\nXjUsBWZJ2kvSRLKHNPuH7dMPHJW/Pxy4PiIijy+UNEnSXsAs4GZJkyVtDyBpMvBW4M4GbtPMzKxu\nTciNXaGhKSsdVw7tO1W2XVEc/vHNxfHLEs1cvGNiw+sT8d8l4qnySrsn4qkyjJsScWDnKYkNqXt4\nOhF/rDh8WOrXuV0S8YsT8W+n/rbLV/awkVqrEbFR0nHA1XlT50XEMkknAwMR0Q+cC1wkaRB4hKzT\nTr7f5cByYCNwbERskrQrcGX23CcTgO9HxH80cJlmpdBx+bFpBovD149sdx5JxN+RiCfyUHJ4IJV7\nIV1G+OWJ+KsT8VQOvDIRf9M/FMf1isQBRyfi5eI65PVzOTSzJmhGrdWIWAwsHhY7seL9BuC9iWNP\nAU4ZFruPdJV9M6vO+dGsQa5DbmYtVZZRADMzs3qVJTc2Oofc5dDMmqQs8+TMSsL50awJypIbGx0h\nr1kOLf9BdAzAnnvu2eDpzHpbGUYBzEqian50bjSrXxlyY0Mj5PWUQ4uIRRExJyLmTJs2rZHTmfW0\nsowCmJVBrfzo3GhWn2blRknzJa2QNCjp+ILtkyRdlm9fIqkvj0+RdIOkpySdOeyY/SXdkR9zRr7y\n9aiMeoQ8L4E2LiKerCiHdvJo22uKgfSmZYlqKosS+6ceGP+Px4vjh/ysOK7Uc/Wpp7lHWmWl2r/E\nXRPx/RLx1DX9pDj8858Wxw9KVJz5TWL/vX9/SfGGbb+fuKDeVJZ5cma9riPzY9P8V3H4gcTuiWQ6\neXhB1ty/X1gcn/eyRPupqizVHkWfnYinfi+6IxG/IRH/aCL+YCK+2y8SG1xlBZqTGyWNB84C5pGt\nRL1UUn9ELK/Y7Wjg0YiYKWkhcBpwBLAB+Aqwb/6q9B3gr4ElZAUV5gNXjeYaGxkh3xW4SdLtwM3A\nT10OzWz0PEJu1jOcH82apEm5cS4wGBH3RcRzwKVsXVt5AXBB/v4K4GBJioinI+Imso75C9cl7Qbs\nEBG/ytfzuBA4bMQ3mBv1CLnLoZk1n0fIzbqf86NZc9WRG6dKqpwnsSgiKidBTAfur/i8iq1XkNm8\nT76ux+PAFCBVunR63k5lm9NrX2oxlz006xDj8Ci4mZlZpTpz4/puX3yr0bKHZtZE42q8zMzMyqYJ\nuXE1sEfF5xl5rHAfSRPI1jV/uEabM2q0WTfneLMOIWBijZeZmVmZNCk3LgVmSdpL0kRgITD80eJ+\n4Kj8/eHA9fnc8EIRsQZ4QtIb8uoqHwJ+VN/lbK23pqwcmt406/Li+A8S+++ciB+biF+XiPf9aWJD\nanbhLon4Ton49ok4pKum7PzHiQ2Jx9gP/3Fh+KBUzYBERZi9ZxTHeSwR3zYR71HCvyGbWZe6OhH/\nfHF490SVlXnvS7TzpUT8jxNJ86lHEgeQ7sFN/FZxfO+zi+OTf1McT+Xe5LyLp1IbjObkxnxO+HFk\n/1LHA+dFxDJJJwMDEdEPnAtcJGkQeISs055dg7QS2AGYKOkw4K15hZZPAOeT9ViuYpQVVqDXOuRm\nXWxoFMDMzMwyzcqNEbGYrDRhZezEivcbgPcmju1LxAfYuhTiqLhDbtZBPEJuZma2pTLkRnfIzTrE\nUK1VMzMzy5QlN7pDbtYhvFKnmZnZlsqSG90hN+sQZRkFMDMzq1dZcmNvdcg//GfJTRNX3FgY//Gp\nxft/d4Sn3pCI/+aM4vjecxMHzErE1xWHn/hZ+ppWJOKvO+XXxRu+uE1x/LnicPyuOK5dEyd+6Qjj\nJVSGUQAz62YfKA5/pLgaF78oDt/zuUTzTybipyTiRyaqqVSrQPZMIv7OVJcoEU8UJmOHVLm0RI6l\nLxG3IWXIjb3VITfrYmUZBTAzM6tXWXKjO+RmHaIs8+TMzMzqVZbc6A65WYcoyyiAmZlZvcqSG8tQ\n2tGsKwyNAlR71WxDmi9phaRBSccXbJ8k6bJ8+xJJfRXbTsjjKyQdMuy48ZL+R9JPRn2DZmZmI9SM\n3NgNPEJu1iEaHQWQNB44C5gHrAKWSurPl/cdcjTwaETMlLQQOA04QtJssmWC9wF2B66VtHdEbMqP\n+xRwF9nSwWZmZi1RlhHyHuuQfz696WvFVVbemSpFkhoHTD25/e5E/NBE/E2JeOoh7F8Wh3eoUqHk\ndcsTG1J/TWtvLo5fWxzWpEQ7qUoxVyfiSpWcKZ8Gf9OfCwxGxH0Aki4FFgCV/xIWACfl768AzpSk\nPP9Jk64AACAASURBVH5pRDwL/FbSYN7eLyXNAN5OVufgs41dopl1t9cXh9+e+Dk+PZFXViaaT+XA\nnRLxNyS+6H/u+cQBwMQ/TWz4aXH4iUQynZY6wcZEfJ9EfP9UQ5brlVHwanqsQ27WveocBZgqaaDi\n86KIWJS/nw7cX7FtFVtnz837RMRGSY8DU/L4r4YdOz1//y2yX+OqFRIzMzNrOo+Qm1lLiboe6lgf\nEXPG/GJykt4BrI2IWyQd1KrzmpmZQd25seu5Q27WIQRMbKyJ1cAeFZ9n5LGifVZJmgDsCDxc5dh3\nAe+S9DayL5N3kHRxRPxlY5dqZmZWWxNyY1cowy8dZl1jXI1XDUuBWZL2kjSR7CHN/mH79ANH5e8P\nB66PiMjjC/MqLHuRPQlwc0ScEBEzIqIvb+96d8bNzKyVGsyNXcEj5GYdYhyNjQLkc8KPI3t8djxw\nXkQsk3QyMBAR/cC5wEX5Q5uPkHWyyfe7nOwB0I3AsRUVVszMzNqi0dzYLXqsQ/729Cb9vjj+w1WJ\nA+5NxJ9KxKcn4n2J+IZE/Ozi8Jv+sTi+NNEMwMcT8dMT8Zcn4ksS8dmJ+O8S8T9JxPliakPpNPqb\nfkQsBhYPi51Y8X4D8N7EsaeQVVJJtf1z4OcNXqKZdbW+RDxRoWS/x0YW5xeJ+KJEPFHRZGK17s3L\nEk1dUhwfPvFvyKsSlWUiUVlGByQaSsVtSK+MgldT8x4lnSdpraQ7K2K7SLpG0j35nzuP7WWa9b6h\nJ8mrvcysczg/mo29ZuXGsVg4T9JnJC2TdKekSySlCnfWVM8vHecD84fFjgeui4hZwHX5ZzNrQFlW\nIzPrIefj/Gg2ppq0ivXQwnmHkn2/f2S+IF6lzQvnkc0lOC0/tnLhvPnA2fnq1dOBTwJzImLf/FIW\njvY+a3bII+JGsrmmlRYAF+TvLwAOG+0FmNkLxo+r/jKzzuH8aNYaTciNmxfOi4jngKGF8ypV/r97\nBXDw8IXzIuK3wNDCeZBN/d42r1r2YuCB0d7jaFP8rhGxJn//ILBrakdJx0gakDSwbt26UZ7OrAQ8\nRG7WC+rKj86NZnWqLzdOHfr/KX8dM6yVooXzhj/8t8XCeUDlwnlbHRsRq4FvAP8XWAM8HhE/G+1t\nNjzmlpdMiyrbF0XEnIiYM21acp1ZM/MkcrOeUi0/Ojea1am+3Lh+6P+n/JV6Crh5l5U9H7IA2AvY\nHZgsadRlgUdbZeUhSbtFxBpJuwFrR3sBrZOaZz9zhPFm+VEi/s/F4W8kdn9plVNcloinqqPcmIjf\nk4i/LhFPPZH+5UTcT5hn6ln94JlWXIiZNaAL82MzTB1hPOX2RDxVmSzRjVn9m/QppieOmbBDcfxV\nfcXxWxLVVPbfJXHigxPxGYm4Ac3KjWOxcN5bgN9GxDoAST8E/hS4uObVFBjtCHnl4iJHke5dmtlI\nlGH1A7Pe5vxo1myN58amL5xHNlXlDZJenM81Pxi4a7S3WHOEXNIlwEFk83NWAX8HnApcLulosqrT\n7xvtBZhZbuhrOTPrCs6PZi3QhNw4RgvnLZF0BXBrHv8f0gXza6rZIY//x97dx9tVlnf+/3wJBlHk\nQRIQEjQMxIdAK2qKtDAOlVJCtUZblFDrMB0qnV+hanXGAfsT/TFlRvpqRTqibSoMiJSHQR0zLUot\n0FraGggU0YBpI0QJBkgAAVGgwev3x1oHdg7r2mefs/fZZ+19f9+v136x97Ue9r1Csq773Gfd1x1x\nUrIp+92Lmc3ExMQVMxsJzo9mQzCg3DgbC+dFxEeofhDv25it1Gk2wjxCbmZmtqNCcqM75GZt4hFy\nMzOzHRWQG90hH5iNSfw9zeGHv9wc/53kNG9L4t2mD2TVVLLJ6n+bxI9K4jcn8bXZDIsvJPHpzsIf\nU4WMApiZ5X6YxJPuylfvao53K1ySVVnJKn59PsnXG5LTvC4rf/bKJO6uWFeF5Eb/LTBrCz9DbmZm\ntqNCcqM75GZtUcgogJmZWc8KyY3ukJu1RSGjAGZmZj0rJDe6Q27WFoWMApiZmfWskNzoDrlZmxQw\nCmBmZjYtBeRGd8in7bYkflVz+DvJ7OzPJaf5hyT+2iT+dBKHap2pJi9O4mck8fOS+K9kX/znSfy4\n7ACDYkYBzMxyWYWSzc3hY3++OX77DV2+I6mK9ujtzfFfTZLm7VmSzaqubUrirjTWVSG50R1ys7YQ\nkFWMNDMzK1EhubGASzQbEQLmT/Ga6hTSCkkbJG2U9JzfeUjaRdKV9fa1kpZ0bDuzjm+QdFwde76k\nmyR9Q9J6Sf9f/xdqZmbWowHkxlHgDrlZm+w0xasLSfOAC4DjqZaFOknS5OWhTgEejoiDqR5GOrc+\ndhmwCjgEWAF8qj7fk8AbI+LVwGHACklH9H2dZmZmveojN46KMbkMszHQ/yjA4cDGiLgrIp4CrgBW\nTtpnJXBJ/f5q4BhJquNXRMSTEXE31UOQh0dlYum859WvmPE1mpmZTYdHyM1sqCaek5v5KMAi4J6O\nz5vrWOM+EbEdeATYu9uxkuZJug14APhqRKyd3oWZmZnNUP+5cSR4Umfq20n89c3h259qjr8jOc0H\nknj2k953k/jCJA5wXxI/KonfkcT/NIkf9uFkw68mcf9166q3meQLJK3r+Lw6IlbPWpuAiHgaOEzS\nnsAXJR0aEd+aze80s1K9Oom/M4lf0hzev9t3vKk5/PT/bo5/JammsiJJ2N9J+gMHZWMZy5O4Aa6y\nYmZD1ttqZNsiIrt73wsc0PF5cR1r2mezpJ2BPYAHezk2In4g6QaqZ8zdITczs9lXyEqdYzLQbzYG\nJkYBur26uxlYKulASfOpJmmumbTPGuDk+v0JwPUREXV8VV2F5UBgKXCTpIX1yDiSdgWOJf/1kZmZ\n2WD1nxtHgkfIzdqkj1GAiNgu6XTg2vpMF0XEeklnA+siYg1wIXCppI1US0etqo9dL+kqqgeXtgOn\nRcTTkvYDLqkrruwEXBURfzHzVpqZmU1TASPk7pCbtcUAnpOLiGuAaybFzup4/wTw9uTYc4BzJsVu\nB17TX6vMzMxmaEDPkEtaAZxP1b3/TER8bNL2XYDPAq+jepTzxIjYVG87k6ps8NPAeyLi2jq+J/AZ\n4FCqCmT/MSL+cSbtc4fcrC0KeU7OzMysZwPIjR3rdBxLVUXsZklrIqKznMUz63RIWkW1TseJk9bp\n2B/4a0kvrwsenA98JSJOqB8VfcFM2+hnyM3aopDn5MzMzHo2mNw48HU6JO0BvIHqUVAi4qmI+MFM\nL3PKEXJJFwFvBh6IiEPr2EeBdwNb690+VP+qfAStS+LXNodvSsoZ/VVymt9L4lcm8VOT+POT+L5J\nHPISilnlpROT+KL/kmx4YxL/0jS/ONPt4o5M4q9M4ntO87vniEfIzUbG+OfHufCSJH5MEk/6P7t/\nMf+KHyblDff63eb4y85LTpTkm6dvT/b/RhJ/Iolnib9AU+fGqUoCN621MbmO9Q7rdEjqXKfj65OO\nXQT8mOrf+f+S9GrgFuC9EfF4L5c0WS8j5BdTlTmb7LyIOKx++WZj1i+PkJuNmotxfjSbXb3lxm0R\nsbzjNavrc9R2Bl4LfDoiXgM8Dpwx05NN2SGPiK9RVWMws9k08Zxct5eZtYbzo9kQDCY3TmedDnpc\np2MzsLlj9eqrqTroM9LPM+SnS7pd0kWS9sp2knSqpHWS1m3dujXbzcw8Qm42LqbMj86NZj0aTG4c\n+DodEXEfcI+kV9THHEO+5vmUZtoh/zRwEHAYsAX4o2zHiFg98SuEhQu7rfNuZuw0xcvM2q6n/Ojc\naDYNfebGiNgOTKzTcSfVmhrrJZ0t6S31bhcCe9frdLyf+vGTiFgPTKzT8RXqdTrqY34HuEzS7VT/\n5v/7TC9xRmUPI+L+ifeS/gzwQiFm/dqJfCKumY0E50ezARtQbhz0Oh11/DZgef+tm2GHXNJ+EbGl\n/vg24FuDaMzs+mFzOH6mOZ4tDv6WJP6BJP7ZJP6rSTwrBvKvSfyIJA47zgnu9LYkvuhdyYbtSfzs\n5vDDNzTHr05OkxUJejKJA/ynJL7g95MNWbmblvEouNlIG8382CZZEnxnEk+qr8zPKm4B8y9JNryi\nOXxQsvsDSTWV9Jnm9Un8viS+JDtReQrIjb2UPbwcOJqqpMxm4CPA0ZIOo1qVaBPwW7PYRrMyCI+Q\nm40Q50ezISgkN07ZIY+IkxrCF85CW8zKJooYBTAbF86PZkNQSG6c0SMrZjYLJmaSm5mZWaWQ3OgO\nuVmbuNa4mZnZjgrIje6Qm7VFIaMAZmZmPSskNxbUIX+iOZwtavyNJP4bSTybcJBNDF+SxLNqKj+b\nxP8giQP8ehI/cFmy4W+bw9/7XnP808lpmp6qBHhJEs98scu2yetrTfjU/9sc1whUWZlYjczMzCbJ\nKpFsTOJJZTWAB77fHN8nK/mVyJaAeVF2QFZwZ5rfW5pCcmNBHXKzlitkFMDMzKxnheRGd8jN2qKQ\nUQAzM7OeFZIb3SE3a4tCRgHMzMx6VkhudIfcrE0KGAUwMzOblgJyozvkZm1RyCiAmZlZzwrJjQV1\nyJNZzJclu7+iOXzzx5rjP5NVUzkrie+fxH+SxN+QxLs5Idvw7SS+oDn80mSJrA8kjb08OX1WueZL\nSTz7s4B8FvtfJ/Fju5yrLQawGpmkFcD5VOMJn4mIj03avgvwWeB1wIPAiRGxqd52JnAK8DTwnoi4\nVtIB9f77Ui0Fvjoizu+vlWZmm6YZT6qAcX0Svy3/6n1+Ltmwpjn8d8nutybxldkXJzmW7dkBBl6p\n08yGTOTlM3s5XJoHXED148dm4GZJayKiszjXKcDDEXGwpFXAucCJkpYBq4BDqH5c/GtJL6fKFB+I\niFslvQi4RdJXJ53TzMxsdvSZG0dFAT9zmI2QnaZ4dXc4sDEi7oqIp4AreO5YzUrgkvr91cAxklTH\nr4iIJyPibqrivodHxJaIuBUgIh4D7gQW9XWNZmZm09FfbhwJHiE3a4v+RwEWAfd0fN4MvD7bJyK2\nS3oE2LuOf33SsTt0vCUtAV4DrO2rlWZmZr0qZITcHXKztujtObkFktZ1fF4dEatnrU01SbsBnwfe\nFxGPzvb3mZmZAcU8Q17AJZqNiImZ5N1esC0ilne8Ojvj9wIHdHxeXMdo2kfSzsAeVJM702MlPY+q\nM35ZRHyh7+s0MzPrVW+5cerTSCskbZC0UdIZDdt3kXRlvX1t/VvhiW1n1vENko6bdNw8Sf8k6S9m\ndoGVgkbIf9gcziZuJ0VZkuIr8LYk/vJ9kg3JH/2Z328Mf+efmnc/KJu0DfDT2e949mwO//MDzfHH\nktNsSOJ7J/Hjkvg3k/jmJA6Q/bV/bRIfhSor0G+t1ZuBpZIOpOpMrwJ+bdI+a4CTgX+kqsNzfUSE\npDXAn0v6ONWkzqXATfXz5RcCd0bEx/tqnZm10BNJPKtS8sg0988qiNyfxDcm8euaw197qjn+hsOT\n8wA//ofmeDZV/YtJfGES/3ISf3n2Z5HkZHtWn3XIZ6PoQUQ8XR/3Xqr5Vbv300aPkJu1RZ+jABGx\nHTgduJbq5nBVRKyXdLakt9S7XQjsLWkj8H7gjPrY9cBVVCnpK8Bp9c3mSOBdwBsl3Va/fmlg12xm\nZtbNYEbIB170AEDSYuBNwGdmenkTChohN2s50fcoQERcA1wzKXZWx/sngLcnx54DnDMpdmPdMjMz\ns+HrLTdONb9qtooefAL4IPnqKD1zh9ysLQpZjczMzKxnveXGbRGxfPYb8yxJbwYeiIhbJB3d7/n8\nyIpZW0yMAnR7mZmZlWQwuXE2ih4cCbxF0iaqR2DeKOlzPV/XJO6Qm7XFgGaSm5mZjY3B5MZnih5I\nmk81SXPNpH0mih5AR9GDOr6qrsJyIHXRg4g4MyIWR8SS+nzXR8Svz/Qy/chK9iPJfc3hF2bnSefW\nJmVQtiTTuZMqIQd9Njl9t6eW/j6Zff7ipJrKXcl5mgu/pJVo0qosW5vD62+a1u4AHJ1VtXlZl4NG\ngUfBzWzgtnXZdmUS/1BzeH2yDEG2XNiLk3iWTP9NEj9oWXP8DUk5rpuSxAJ5gZes4EwWzyqHpcXv\n3pnEF2cH2IT+51dtlzRR9GAecNFE0QNgXUSsoSp6cGld9OAhqk429X4TRQ+282zRg4GaskMu6QDg\ns8C+QFA9KH++pBdT/UteAmwC3hERDw+6gWbF8DPkZiPDudFsSAaUGwdd9GDS9r8B/qaf9vXyyMp2\n4AMRsQw4Ajitrsl4BnBdRCylKhD6nCLrZjYNfobcbJQ4N5oNQyG5ccoOeURsiYhb6/ePUdU3XsSO\n9RovAd46W400K4KfITcbGc6NZkNSSG6c1qTOehnR11A9LbZvRGypN91H9Wu7pmNOlbRO0rqtW7s9\nFWxm7DTFy8xax7nRbJYVkBt7vgxJuwGfB94XETvM6qhnoUbTcRGxOiKWR8TyhQuzdWbNjJ2A+VO8\nzKxVnBvNZlkhubGnKiuSnkd1w7ksIr5Qh++XtF9EbJG0H5CU7miLaRaUSSqFvD/Z/fw/TzYclVRT\n+VGy/wuSeDaA0q3KSvYdWXWUrMpK5iVJPPvH8Uhz+JDG8SNgzy7fnZX/f3WXY0bBmPykb1aC0cmN\nm7ps+05z+O6kmkpSgYykCMpz1kKcoKycyvObw9uSXLohOc09SRzyqilZW7NKY3+axH8l++JfSuLJ\nNduzCsiNU16iJFGVgrkzIj7esamzXuPJwJcG3zyzkgi0S/eXmbWCc6PZsJSRG3sZNj4SeBfwTUm3\n1bEPAR8DrpJ0CvBd4B2z00SzUoip/0k+OYyGmNnUnBvNhqKM3DhlhzwibqT602hyzGCbY1YyMfWv\nLh8fRkPMbArOjWbDUkZu9EqdZq3RyyiAmZlZScrIjeN/hWYjYyc8ucfMzKxTGbmxoA75bs3hX092\nf6g5/OaNzfFHL26O755UFuFnk/grk3hWPSSbqA756lVZEf1/TeLrkvjV0/zef0niWdWX45M4wL9L\n4q96d5eDRsGYLDlmZi1yf5dta5vDByYlv76YlO+6NTl9ViFs/6SsV1ZdK6sMmeXGI7vUwlv/VHM8\nq6byi0n8vY2rrAOnJPEj8jbZFMY/NxbUITdruzJGAczMzHpXRm50h9ysNcp4Ts7MzKx3ZeTG8b9C\ns5HRy0xyMzOzkpSRG90hN2uNMkYBzMzMeldGbixgMVKzUTHxnFy3V3eSVkjaIGmjpDMatu8i6cp6\n+1pJSzq2nVnHN0g6riN+kaQHJH2rzws0MzObpv5z4ygY/x85npFM3f5AsvsvNId/Ltn9hf+pOX7v\nnzTHF52UnGhZEs+qqWQVSiCvapJNVs5mt/98En9zEs9+zMtm2/9TEn9NEgc48g3JhlO7HNR2/Y0C\nSJoHXAAcC2wGbpa0JiLu6NjtFODhiDhY0irgXOBEScuAVcAhwP7AX0t6eUQ8DVwMfBL47IwbZ2Zz\nKCkPBsALk3iSEP4iqbKyMjnNAUk8y2m775NsODKJ/31z+NEHkv2BFyXxVyTxnX832fDBJP6S/Ltt\nBjxCbmZDNfGc3IxHAQ4HNkbEXRHxFHAFz02TK4FL6vdXA8dIUh2/IiKejIi7qTL44QAR8TXSQqBm\nZmazqe/cOBLcITdrjYlRgG4vFkha1/Hq/JXAIuCejs+b6xhN+0TEduARYO8ejzUzMxuynnLj1GcZ\n8COdkg6QdIOkOyStl/Tefq5y/H8HYDYyeqq1ui0ilg+hMWZmZi3Qfx3y2XikE9gOfCAibpX0IuAW\nSV+ddM6eeYTcrDX6HgW4lx2f2Fxcxxr3kbQzsAfwYI/HmpmZDdlARsgH/khnRGyJiFsBIuIx4E76\n+M2yO+RmrSFglyleXd0MLJV0oKT5VD/Rr5m0zxrg5Pr9CcD1ERF1fFX9K7sDgaXATX1fkpmZWV96\nyo3dHueEWX6ks3685TXA2plcIfiRFbMW6W8meURsl3Q6cC1VLZ2LImK9pLOBdRGxBrgQuFTSRqqJ\nmqvqY9dLugq4g+rXcKfVFVaQdDlwNNUNbzPwkYi4cMYNNTMz61lPuXHOHueUtBvweeB9EfHoTM9T\nUId8t+bwgqub47f9WmP4hR98qnn/pKTfoqOS5iRfy/eT+DuS+H5ZiSjg8FcmGw5J4i9L4odN8zxZ\nyafkr9ubkt2L0/9qZBFxDXDNpNhZHe+fAN6eHHsOcE5DPCvSaWYjYXGXbUluzI75kyRJPZ2c5lX7\nJxuyPJEtd/CNJP665vDuhyb7A7snx/D6JJ79+RXUhZpTA1mpczqPdG7u9ZFOSc+j6oxfFhFf6KeB\nfmTFrDUGM5PczMxsfAwkNw78kc76+fILgTsj4uN9XCD0ehVmNgz9zyQ3MzMbL/3nxtl4pFPSUcC7\ngG9Kuq3+qg/Vv6meNnfIzVqjjNXIzMzMejeY3DjoRzoj4sa6cQPh7G/WGgN5Ts7MzGyMlJEb3SE3\naw2PkJuZme2ojNw45RVKOgD4LLAvEMDqiDhf0keBd/NsfZEZPzczHNml/moSv6U5/AfvaY7fdkNz\nPJt5/lNJfP4bkg1vTOJdZpLz6iS+JImP/1/4dvMz5GajYrRyY1YRC9J8sO2LzfEfTPOrH02qsuye\n3etOTOJZPssqo3TLjXt22WbtU0Zu7KUH1rg0aL3tvIj4w9lrnllp/EOR2YhwbjQbmvHPjVNeYURs\nAbbU7x+T1NfSoGaW2YkeVuM0sxZwbjQbljJy47TqkDcsDXq6pNslXSRpr+SYUyeWMt26NVk9x8xw\nHXKz0eTcaDabysiNPXfIG5YG/TRwENUyjluAP2o6LiJWR8TyiFi+cOHCATTZbFxNzCTv9jKzNnFu\nNJttZeTGnn6saFoaNCLu79j+Z8BfzEoLzYpRxsQVs3Hh3Gg2DGXkxl6qrDQuDSppv/oZOoC3Ad+a\nnSbOlWyG9vXN4cNmrSFWjDJKO5mNg9HKjQd32fbbzeEFr0jia5vjqZcl8SzHvi6JvySJj39HzcrI\njb1c4ZE0LA0KnCTpMKpyT5uA35qVFpoVo4zFD8zGhHOj2VCUkRt7qbKSLQ3a4prjZqOojFEAs3Hg\n3Gg2LGXkxvG/QrORUcZzcmZmZr0rIze6Q27WGmWMApiZmfWujNw4/ldoNjLKeE7OzMysd2XkRnfI\nzVqjjFEAMxu2bveVrAJLFvccVRu2MnLj+F+h2cgQJSwPbGZm1rsycqM75GatUcYogJmZWe/KyI07\nzXUDzGzCxEzymS8PLGmFpA2SNko6o2H7LpKurLevlbSkY9uZdXyDpON6PaeZmdns6T83Qvvzozvk\nZq0xMQrQ7dXlaGkecAFwPLCMaoGSZZN2OwV4OCIOBs4Dzq2PXQasAg4BVgCfkjSvx3OamZnNkv5y\nI4xGfnSH3Kw1JmaSz3gU4HBgY0TcFRFPAVcAKyftsxK4pH5/NXBMvQT4SuCKiHgyIu4GNtbn6+Wc\nZmZms6Tv3AgjkB+H+lDOLbfcsk3Sd+uPC4Btw/z+Fijtmkf1el82F196yy23Xis9b8EUuz1f0rqO\nz6sjYnX9fhFwT8e2zcDrJx3/zD4RsV3SI8Dedfzrk45dVL+f6pxm1gfnRl/ziBjV3AgjkB+H2iGP\niIUT7yWti4jlw/z+uVbaNZd2vf2KiBVz3QYzGz7nRl+z5UrJjX5kxWx83Asc0PF5cR1r3EfSzsAe\nwINdju3lnGZmZm3W+vzoDrnZ+LgZWCrpQEnzqSahrJm0zxrg5Pr9CcD1ERF1fFU9y/xAYClwU4/n\nNDMza7PW58e5LOy4eupdxk5p11za9c6p+pm304FrgXnARRGxXtLZwLqIWANcCFwqaSPwENUNhHq/\nq4A7gO3AaRHxNEDTOYd9bWYFKfG+6Wu2WTUK+VFV59/MzMzMzOaCH1kxMzMzM5tD7pCbmZmZmc2h\noXfIS1mGW9JFkh6Q9K2O2IslfVXSv9T/3Wsu2zhIkg6QdIOkOyStl/TeOj6212xmNkgl5EfnRudG\nazbUDnlhy3BfTLXEaqczgOsiYilwXf15XGwHPhARy4AjgNPq/7fjfM1mZgNRUH68GOdG50Z7jmGP\nkBezDHdEfI1qlm6nzmVZLwHeOtRGzaKI2BIRt9bvHwPupFrJamyv2cxsgIrIj86Nzo3WbNgd8qal\nSxcl+46jfSNiS/3+PmDfuWzMbJG0BHgNsJZCrtnMrE8l58ci8oRzo3XjSZ1zpC42P3Y1JyXtBnwe\neF9EPNq5bVyv2czMBmNc84Rzo01l2B3y0pfhvl/SfgD1fx+Y4/YMlKTnUd1wLouIL9Thsb5mM7MB\nKTk/jnWecG60Xgy7Q176Mtydy7KeDHxpDtsyUJJEtcrVnRHx8Y5NY3vNZmYDVHJ+HNs84dxovRr6\nSp2Sfgn4BM8uM3rOUBswJJIuB44GFgD3Ax8B/g9wFfBS4LvAOyJi8uSWkSTpKODvgG8CP6nDH6J6\nVm4sr9nMbJBKyI/OjYBzozUYeofczMzMzMye5UmdZmZmZmZzyB1yMzMzM7M55A65mZmZmdkccofc\nzMzMzGwOuUNuZmZmZjaH3CE3MzMzM5tD7pCbmZmZmc0hd8jNzMzMzOaQO+RmZmZmZnPIHXIzMzMz\nsznkDrmZmZmZ2Rxyh9zMzMzMbA65Q94ykv5E0ocHva+Zmdmocm60caeImOs2FEPSJmBfYDvwNHAH\n8FlgdUT8pM9zHw18LiIWz+DY+cA3gBfN5HgzM7OZaltulPRR4PeAJzvCPx0Rd/XTFrNuPEI+fL8c\nES8CXgZ8DPivwIVz2yT+C7B1jttgZmblaltuvDIidut4uTNus8od8jkSEY9ExBrgROBkSYcCSLpY\n0u9P7Cfpg5K2SPq+pN+UFJIO7txX0guBLwP7S/ph/dq/l3ZIOhD4deB/DPoazczMpqMtudFs2Nwh\nn2MRcROwGfi3k7dJWgG8H/gF4GDg6OQcjwPHA9/v+Gn++5KOkvSDKZrwP4EPAT+e+VWYmZkNOSck\nFwAAIABJREFUTgty4y9LekjSekn/Tz/XYtYLd8jb4fvAixvi7wD+V0Ssj4gfAR+dzkkj4saI2DPb\nLultwLyI+OJ0zmtmZjYEc5IbgauAVwELgXcDZ0k6aTrfYTZd7pC3wyLgoYb4/sA9HZ/vadhnRupf\n5f0B8J5BndPMzGyAhp4bASLijoj4fkQ8HRH/AJwPnDDI7zCbbOe5bkDpJP0M1U3nxobNW4DOmeEH\ndDnVdMvlLAWWAH8nCWA+sIek+4AjImLTNM9nZmY2EHOYG7NzaADnMUt5hHyOSNpd0puBK6hKMn2z\nYbergN+Q9CpJLwC61VW9H9hb0h49NuFbVDexw+rXb9bnOIwBjzaYmZn1ogW5EUkrJe2lyuFUv0n+\n0jQuw2za3CEfvv8r6TGqTu/vAR8HfqNpx4j4MvDHwA3ARuDr9aYnG/b9NnA5cJekH0jaX9K/lfTD\n5NzbI+K+iRfVrwV/Un9+us9rNDMzm45W5Mbaqvq8j1HVQz83Ii6Z2WWZ9cYLA40QSa+iGtneJSK2\nz3V7zMzM5ppzo40Dj5C3nKS3SdpF0l7AucD/9Q3HzMxK5txo48Yd8vb7LeAB4DtUSwq7HqqZmZXO\nudHGih9ZMRsj9YIZ5wPzgM9ExMcmbd+F6pnI1wEPAidGxCZJewNXAz8DXBwRpzecew3wbyLi0Fm+\nDDMzs6J4hNxsTEiaB1xAtTLdMuAkScsm7XYK8HBEHAycR/WrXoAnqCoV/Ofk3L8CdJsEZWZmZjPU\nVx3yqUbjJluwYEEsWbKkn680m3WbNm1i27ZtQ685u7M05e+rfgLXRsSKZPPhwMaIuAtA0hXASuCO\njn1W8uyqdlcDn5SkeonpGyUdPPmkknajWqb6VKpyY2Y2henkR+dGGwUjnBtHwow75B2jcccCm4Gb\nJa2JiDuyY5YsWcK6detm+pVmQ7F8+fI5+d4AXjjFPo/BKyV1/iNaHRGr6/eL2LGG/Gbg9ZNO8cw+\nEbFd0iPA3sC2Ll/734A/An40RfPMjOnnR+dGGwUtz40LhtGW2dTPCHkvo3Fm1iNRDaVNYVtEDO2u\nKOkw4KCI+F1JS4b1vWYjzvnRbEB6zI0jr59nyJtG4xZN3knSqZLWSVq3devWPr7ObLwJeN4Urync\ny45LSC+uY437SNoZ2INqcmfmZ4HlkjZRLWH9ckl/M3VTzIo2ZX50bjTrzQBy40iY9UmdEbE6IpZH\nxPKFCxfO9teZjbR5U7ymcDOwVNKBkuZTrTa3ZtI+a4CT6/cnANdHl1JLEfHpiNg/IpYARwH/HBFH\n935FZtbEudGsd33mxpHQzyMrvYzGmVmPJkYBZqp+Jvx04Fqqe9RFEbFe0tnAuohYA1wIXCppI/AQ\nVae9+v5qFHx3YL6ktwK/2G1OiJmlnB/NBqTf3Dgq+umQPzMaR3WjWQX82kBaZVagQTwnFxHXANdM\nip3V8f4J4O3JsUumOPcmwDXIzabm/Gg2IKU8Qz7jDnk2GjewlpkVppRRALNx5/xoNjil5Ma+6pA3\njcaZ2cx5pS6z8eD8aDY4JeTGvjrkZjY4OwHz57oRZmZmLVJKbnSH3KxFShgFMDMzm44ScqM75GYt\nIcoYBTAzM+tVKbnRHXKzlhBljAKYmZn1qpTc6A65WUuUMpPczMysV6XkRnfIzVqkhFqrZmZm01FC\nbnSH3KwlShkFMDMz61UpudEdcrOWKGU1MjMzs16VkhvdITdriVJGAcxsHD2RxDcl8Wzh0mz/B5P4\n3kl8cRIHWJTEX5LE95xmPOMu10yUkhv9t8OsRUoYBTAzM5uOEnKjO+RmLbETZYwCmJmZ9aqU3OgO\nuVmLlDAKYGZmNh0l5MYSaq2bjYSJ5+S6vczMzEoyqNwoaYWkDZI2SjqjYfsukq6st6+VtKSO7y3p\nBkk/lPTJjv1fIOkvJX1b0npJH+vnOt0hN2uJQdx02n7DMTMzm44B5cZ5wAXA8cAy4CRJyybtdgrw\ncEQcDJwHnFvHnwA+DPznhlP/YUS8EngNcKSk46dxaTvwIyvTtj2JfymJX9Yc3vbF5vgLk9Ps+nPJ\nhj9N4gCHdtlmbdNvaaeOG86xwGbgZklrIuKOjt2eueFIWkV1wzmRZ284h/Lcvzh/GBE3SJoPXCfp\n+Ij4ch9NNbORlVVT+XwS39gcjo82x7NiKvcn8X2T+IIXJBsgz41Jfv/erc3xhclpdt0n2fAHSXxl\nEp9uFZfxNKCyh4cDGyPiLgBJV1D9wXfmx5XAR+v3VwOflKSIeBy4UdLBnSeMiB8BN9Tvn5J0K93L\n+3TlEXKzlhjAKMAzN5yIeAqYuOF0WglcUr+/Gjhm4oYTETcyKdtGxI8i4pkbDtDXDcfMzGw6esyN\nCySt63idOuk0i4B7Oj5v5rn1L5/ZJyK2A4+Q19XcsY3SnsAvA9f1el2TeYTcrEV6GAVYIGldx+fV\nEbG6ft90w3n9pON3uOFImrjhbJvqiztuOOdP3UwzM7PB6CE3bouI5bPfkueStDNwOfDHEyPwM+EO\nuVlL9Lj4wZzcdAZ1wzEzM5uOAS0MdC9wQMfnxXWsaZ/Ndc7bg/whqk6rgX+JiE/000A/smLWEhPP\nyXV7TWE6Nxzm4oZjZmY2HQPIjQA3A0slHVjPh1oFrJm0zxrg5Pr9CcD1ERFd2yb9PlUefV9vzch5\nhNysJQYwCvDMDYeq470K+LVJ+0zccP6R6d9wfrO/5pmZmU3PIEbI60c0TweuperDXxQR6yWdDayL\niDXAhcClkjYCD1Hl0KoN0iZgd2C+pLcCvwg8Cvwe8G3gVkkAn4yIz8ykje6Qm7VIP7+yGoUbjpmZ\n2XQN4nGOiLgGuGZS7KyO908Ab0+OXZKcVgNoGtBnh7xO4I8BTwPb5+qB+uHKSjv9ZXN4fVLe8Jrm\nMP+UxF/zD83x/3JtcgDkpZ1+mMST8lT8fRL/RhLP/lodksRPSeLPT+LjaSdgfp/naPsNx6wUo58f\ns/KGm5L4hiT+t81h/XRz/Pu3N8ezWhcLjk02dHsSL5nD/j+bp8c89Z7m3R9Kzv4EDzTGl/zjf2g+\n4Ig/Sc70W0m8LIPIjaNgECPkPx8RU1ZoMLOpeVKH2VhxfjQbgBJyox9ZMWsJUcYogJmZWa9KyY39\n/tARwF9JuqWhCDsAkk6dKNS+devWPr/ObHyJ6h9kt5eZjYyu+dG50aw3peTGfkfIj4qIeyXtA3xV\n0rcj4mudO9SLlqwGWL58eddqDmYlG1CtVTNrh6750bnRrDel5Ma+frCIiHvr/z4AfJFq6W4zm6EB\n1Fo1sxZwfjQbnBJy44xHyCW9ENgpIh6r3/8icPbAWjbnkkoksao5fk9zmKQ4yj9/sDn+8g8n57kj\nifPdbAN5NZVs5fNkNvyffLU5fmNymluT+OlJ/LdfmWw4JomPp1JGAczG3Wjlx6yaSlZRLKmm8sP/\n1hzf7aXN8R9/rzl+QHOYvV6ebHg8if8giQP3JosNL80PaZKl5Te+M9nw7SR+xHXJBldZgXJyYz+P\nrOwLfLGuS7wz8OcR8ZWBtMqsQBOrkZnZyHN+NBuQUnLjjDvkEXEX8OoBtsWsaKWMApiNO+dHs8Ep\nJTe67KFZS5QyCmBmZtarUnKjO+RmLVHKKICZmVmvSsmN7pCbtUgJowBmZmbTUUJudIc8tbE5nFRN\n4V+T+E81h5NaLdyaTXhflMTZLdtAPqU7mSX/aFJN5eeS03wsiWdPTl6fxH97c7KhLKWMAphZm9yX\nxJM8kVXj2m33ZP8lzeFdX5LEfyk5z+IknlUay/IfsCjJOXc81Rh+KDnNLyfxx7MCL8dmDbo322CU\nkxvdITdriVKekzMzM+tVKbnRHXKzlihlFMDMzKxXpeTGvlbqNLPB2mmKl5mZWWkGkRslrZC0QdJG\nSWc0bN9F0pX19rWSltTxvSXdIOmHkj456ZjXSfpmfcwfq158YKbXaGYtIGD+FK8pz9HyG46Zmdl0\nDCg3zgMuAI4HlgEnSVo2abdTgIcj4mDgPODcOv4E8GHgPzec+tPAu6nWeV0KrOj5wiZxh9ysJUR/\nowCjcMMxMzObjn5zY+1wYGNE3BURTwFXACsn7bMSuKR+fzVwjCRFxOMRcSNVnny2XdJ+wO4R8fWI\nCOCzwFunfYE1P0Oe+svm8LXJ7ic3h+Pg5vit2QNRlyXxjyZxtmcbgC80h7dd2hzPppJ/oDn8wmRy\n++O/0KVJlpoYBejDMzccAEkTN5w7OvZZybN/m64GPjlxwwFulLTD39jOG079eeKG8+X+mmpm7bAt\niWfJ7qDm8I+/1hzfdVNynv+YxA9L4t9K4lmVrm4VyJLvOPa2xvBL/rG5+srjTyenz/7oUkdO94Ci\nDCA3QlWr7p6Oz5uB12f7RMR2SY8Ae5P/I1nEjn8BN9OlJt5UPEJu1iI9jAIskLSu43Vqx+FNN5zJ\nN4cdbjjAxA0nM9AbjpmZ2XT1mRtHgkfIzVqix5nk2yJi+aw3xszMrAUGlBvvBQ7o+LyY5xaAn9hn\ns6SdgT2AB6c4Z2eB/KZz9swj5GYtMVFrtdtrCtO54TAXNxwzM7PpGEBuBLgZWCrpQEnzqdZnXDNp\nnzU8+wDyCcD19bPhjSJiC/CopCPqYgf/HvhSb815LnfIzVpiYhSg22sKrb/hmJmZTccAcuPEI5qn\nUz3hfydwVUSsl3S2pLfUu10I7C1pI/B+4JlKZZI2AR8H/oOkzR0FE34b+AzV8u7foY/5VX5kxawl\n+l2NrJ6EMnHDmQdcNHHDAdZFxBqqG86l9Q3nIapOe/X91Q1nd2C+pLcCvxgRd1DdcC4GdqW62XhC\np5mZDcWgVuqMiGuAaybFzup4/wTw9uTYJUl8HXDoAJrnDnlufXN4/2T3g3ZvDG/l0cb4Ppcn53lH\nEk8rl+ybbSCtFLPgp5vju9/eHH+iOfz44uY4L0riP5XEs1n7hRnEamRtv+GYWdv8MIlnc72TghP3\nNId5eVZBJLulZAUtsifrsmoq3X6Rl1Qni+ZqKmxKTnNDEl+YxBf9XLIhS/wG5azU6Q65WYsMYhTA\nzMxsnJSQG90hN2uJUkYBzMzMelVKbnSH3KwlBvWcnJmZ2bgoJTe6Q27WEqWMApiZmfWqlNzoDrlZ\nS5QyCmBmZtarUnKjO+SppMpKNvH89uZqKulfomwW9suSeLq4+aeyDcB9SfyVzeHvJLu/L4lnC6h/\nM4mnE8kPyzYUpZRRADNrkz2TeFJeK1sXbEl2/qSiSRrPqr48vzl8y/9ojr8kaw+w6Oeb4zquOb4q\n+bNYlbSJ5Dy8KYlnlWIMysmNUy4MJOkiSQ9I+lZH7MWSvirpX+r/7jW7zTQrw05TvMysPZwfzYaj\nhNzYy3VcDKyYFDsDuC4ilgLX0bGakZnNjID5U7zMrFUuxvnRbFaVkhun7JBHxNeoVvTrtBK4pH5/\nCfDWAbfLrEwlDAOYjQnnR7MhKSA3zvQZ8n0jYkv9/j66LBcp6VTgVICXvvSlM/w6swJMDAN0kz3W\naWZt0VN+dG4061EhubHvnysiIoDosn11RCyPiOULF2YzGc0MUcQogFkpuuVH50azHhWSG2c6Qn6/\npP0iYouk/YAHBtmodvh2czgrXHJPc/gF2ekfaQ7fcndz/HV/m5znxXdl3wBHHju9L39V8rf6VQua\n49cl/9tPSL52t09kG5J4YXoZBfjRMBpiZn0Ysfy4OIkn9+UH/rk5vs/bk/NklUh+kMSzoc4k+d6R\n7P5YEgdYlFRRS0uzZFVTjpnmeVzYbkYKyY0z/bliDXBy/f5k4EuDaY5Z4QoYBTAbc86PZoNWQG6c\n8sc1SZcDRwMLJG0GPgJ8DLhK0inAd+lSYdrMelRKsVWzMeH8aDYEheTGKTvkEXFSsin7XY2ZzUQp\ny5GZjQnnR7MhKCQ3jslAv9kYmBgF6Paa6hTSCkkbJG2U9Jz6x5J2kXRlvX2tpCUd286s4xukZ5es\nk/S7ktZL+pakyyVlD4WamZkN1gByI7Q/P7pDbtYm86Z4dSFpHnABcDywDDhJ0rJJu50CPBwRBwPn\nAefWxy4DVgGHUC108ilJ8yQtAt4DLI+IQ+tWrOr7Os3MzHrVR26E0ciPnvKbeeqp5vj9yf7JBPBd\nd2mOn/mW5vi/SU7/ul9INjydxAFYm8T/XXP4xz9pju+6pDm+JCkesNe7k+99ZxI3YBDPyR0ObIyI\nuwAkXUG1SElnHYKVwEfr91cDn5SkOn5FRDwJ3C1pY32+71HdJ3aV9K9UhYO+31crzaxFkipaz8xL\nnWSfDcn+30nieyTxe5P49iSeVGV5V1bdpVth6iQ3fufy5vi9SfwN+yfnPyWJ/3YSz6qyGDCoZ8hb\nnx89Qm7WFhPPyXUfBVggaV3H69SOMyxixwKcm+sYTftExHaqGph7Z8dGxL3AH1LdeLYAj0TEX/V9\nrWZmZr3oPzfCCORHj5CbtUVvowDbImL57DemImkvqtGBA6mGqP63pF+PiM8Nqw1mZlawFuZGGHx+\n9Ai5WVv0NgrQzb3AAR2fF/Pc3ws/s4+knal+n/xgl2N/Abg7IrZGxL8CXwB+bnoXZmZmNkP950YY\ngfzoDrlZW/Q/k/xmYKmkAyXNp5pcsmbSPp2LlpwAXF8v770GWFXPMj8QWArcRPWruCMkvaB+lu4Y\n4M5+LtPMzKxng6my0vr86EdWzNqkj1qrEbFd0unAtfWZLoqI9ZLOBtZFxBrgQuDSelLKQ9Qzwuv9\nrqKa4LIdOC0ingbWSroauLWO/xOweuatNDMzm6Y+65CPQn50hzyTVS/ZlMSTSdivTAqXfPtNyXnu\nSOJZgZJ/n8QB3pqtTZHMVt+a7P7SpIRMVhImq+KSzuY3YCAzySPiGuCaSbGzOt4/ATSWJYiIc4Bz\nGuIfoVqB0MyK8bIkfmQS39Qc/t4NzfGXPpicJ8tbWf5IvpesGgzAtubwQZOr4NUeTBLzLUlBjddd\nkHxv9meXXZu7aMDAVupse370/22zthB+iMzMzKxTIbnRHXKzthAwf64bYWZm1iKF5EZ3yM3apIBR\nADMzs2kpIDe6Q27WFoWMApiZmfWskNzoDrlZWxTynJyZmVnPCsmN7pBndj28Ob7wpub4Wc3h4z6a\nnD9bXPUXkvhz5vbWXrVPsgFgSRLf3Bx+6f7J/smM9KwSzc6H5E2y3IBmkpuZ9e8lSfwVSfyW5vBL\nf7o5vu325viC3ZLz75LEn0zieyZxqCrUNfhxUk0lO1VWaewvH2qOv+n9yQFJB4ITk3hhCsmN7pCb\ntcXEamRmZmZWKSQ3ukNu1haFjAKYmZn1rJDc6A65WZsUMApgZmY2LQXkRnfIzdqikFEAMzOznhWS\nG90hN2uLQp6TMzMz61khudEdcrO2KGQUwMzMrGeF5MYpO+SSLgLeDDwQEYfWsY8C7wa21rt9KCKu\nma1Gzo3jmsPfTcoeXt0cPv/y5PQ/k8Szv3RJNSZ+9ECyAXhdVp7qW9kBzeHt/7c5vvOxyXmW5G2y\n7goYBTAbF+OdH7Nafwcn8Y1JPCkxuOCXk/2/kcSzMoyLkni37k2ybdfFzfGXr0vOk/wZPZaUPbwu\nSeTHfCg5f9IP6VrScUwVkBt7KbV+MbCiIX5eRBxWv0bwZmPWMhOjAN1eZtYmF+P8aDa7CsmNU46Q\nR8TXJC2Z/aaYFa6Q5+TMxoXzo9kQFJIb+1mM9HRJt0u6SNJe2U6STpW0TtK6rVu3ZruZ2QBGASSt\nkLRB0kZJZzRs30XSlfX2tZ2dCUln1vENko7riO8p6WpJ35Z0p6Sf7e9CzcbelPnRudGsRwMaIW97\nfpxph/zTwEHAYcAW4I+yHSNidUQsj4jlCxcunOHXmRVipyleXUiaB1wAHA8sA06StGzSbqcAD0fE\nwcB5wLn1scuAVcAhVL+C/1R9PoDzga9ExCuBVwN39nWNZuOtp/zo3Gg2DX3kRhiN/DijDnlE3B8R\nT0fET4A/Aw6faQPMrLYTMH+KV3eHAxsj4q6IeAq4Alg5aZ+VwCX1+6uBYySpjl8REU9GxN1UM7QO\nl7QH8AbgQoCIeCoiftDPZZqNM+dHswHrPzfCCOTHGZU9lLRfRGypP76NvGzHCPuPzeEvXNsc/05S\nfeWx5PTPT+L3JvGjkvhuv5tsALgliS/ITtYc3vn3k/3flMQLnAE+KP08RFaVG7in4/Nm4PXZPhGx\nXdIjwN51/OuTjl0E/JiqWsT/kvRqqr9U742Ix/tqqdmYGp/8mOQDjkzif5zEk5yZJrusOtiDSfwv\nk/jyJA557tqUxPdoDv/4q83xJclpsrvmbXc1xw/L+nYF5tipc+MCSZ3lcFZHxOqOz63Pj72UPbwc\nOJrqYjcDHwGOlnQYEFR/g39rJl9uZh1ELz/pT3XTGbSdgdcCvxMRayWdD5wBfHgWv9NsJDg/mg1B\nb7lxW0R0+ylsNgw0P/ZSZeWkhvCFM/kyM+tC9DIK0O2mcy9wQMfnxTx3GGpin82SdqYa+nmwy7Gb\ngc0RsbaOX011wzErnvOj2RD0lhun0vr82P8lmtlg9D+T/GZgqaQDJc2nmoSyZtI+a4CT6/cnANdH\nRNTxVfUs8wOBpcBNEXEfcI+kid8jH0O+TJWZmdlgDabKSuvz44yeITezWdJHrdX6mbfTqR7anAdc\nFBHrJZ0NrIuINVSjd5dK2gg8RHVTot7vKqqbyXbgtIh4uj717wCX1Texu4DfmHkrzczMpqnPOuSj\nkB/dITdri4lRgD7UqwJeMyl2Vsf7J4C3J8eeA5zTEL+N7jOkzMzMZscAciO0Pz+6Q55aksQvaw4f\n9J1k/41JPCmzsiibMX5oEj8oicOz1XsmOyaJfzeJH5bED+7y3TZthaxGZmaj7CVJPKtcsjiJ35bE\ns+orWWmyLJ9tTuKQ5+WsgkxS7WTX9c3xq7/fHP9gcvpNSZwnsg1lKSQ3ukNu1hYDGgUwMzMbG4Xk\nRnfIzdqikFEAMzOznhWSG90hN2uLQkYBzMzMelZIbnSH3KxNChgFMDMzm5YCcqM75GZtUcgogJmZ\nWc8KyY3ukE9bVlkkix+XxLPZ07dMrzn59Gzg8ST+YBLP2rRvEt+ty3fbtA1mNTIzszmQVV/J4ll1\nlO3T/N6kAgrXdTnm2iT+50k8K4/y6ubw65MqK5mfZBum+2cxpgrJje6Qm7WFgPlz3QgzM7MWKSQ3\nukNu1iYFjAKYmZlNSwG50R1ys7YoZBTAzMysZ4XkRnfIzdqikIkrZmZmPSskN7pDbtYWhUxcMTMz\n61khudEd8jnzwyS+OYnPZLb1xiSezAxPq6Zks+RtoAr5tZyZjaP7kniW676bxLNct0sSXzDNOMDz\nk/ghSTy7tqQL9XSye1bILNvfXbRKIbnR/7fN2qSAUQAzM7NpKSA3ukNu1hY7UcQogJmZWc8KyY0F\n/MxhNkJ2muJlZmZWmgHkRkkrJG2QtFHSGQ3bd5F0Zb19raQlHdvOrOMbJB036bh5kv5J0l/M8Oqg\n98sws1k3MZO822uqU7T8hmNmZjYtg8mN84ALgOOBZcBJkpZN2u0U4OGIOBg4Dzi3PnYZsIpqksEK\n4FP1+Sa8F7hzZhf3LHfIzdpCwLwpXt0OH4EbjpmZ2bT0mRtrhwMbI+KuiHgKuAJYOWmflcAl9fur\ngWMkqY5fERFPRsTdVBUzDgeQtBh4E/CZmV7ehCmfIZd0APBZYF8ggNURcb6kFwNXAkuATcA7IuLh\nfhtUjmzm+SNJfPLfm9pTv5l/xfyfTzZsSOKvS+J75t9hg9N/rdVnbjgAkiZuOHd07LMS+Gj9/mrg\nk5NvOMDdkiZuOP/YccM5B3h/Xy00GxPl5sYsd12WxB9M4pck8aSiyQ9/0hzf7bXJebrJqqlk8R8k\n8SSXPpTsnnUcX5TEPc2v0ltuXCBpXcfn1RGxuuPzIuCejs+bgddPOscz+0TEdkmPAHvX8a9POnZR\n/f4TwAfp8n+xV72MkG8HPhARy4AjgNPq0bQzgOsiYilwXf3ZzPox9SjAAknrOl6ndhzddMNZxI52\nuOFQ/QS49xTHTtxwkoxoViTnRrNhmTo3bouI5R2v1dmpBkXSm4EHIuKWQZxvyh+/ImILsKV+/5ik\nO6kS9Urg6Hq3S4C/Af7rIBplVqTeRgG2RcTy2W9MpfOGI+noYX2vWds5N5oNyWBW6rwXOKDj8+I6\n1rTPZkk7A3tQ/YonO/YtwFsk/RJVcfvdJX0uIn59Jg2c1jPk9QSw1wBrgX3rGxJUv2PadyYNMLNa\n/8/JTeeGQ483nCOpbjibqJ65e6Okz03ruszGnHOj2SwazDPkNwNLJR0oaT7VnKk1k/ZZA5xcvz8B\nuD4ioo6vqosiHAgsBW6KiDMjYnFELKnPd/1MO+MwjQ65pN2AzwPvi4hHO7fVDY7kuFMnfr2+devW\nmbbTbPz1P5O89Tccs3Hj3Gg2ywZQZaV+RPN04FqqAgVXRcR6SWdLeku924XA3vUcqvdTP24WEeuB\nq6jmY30FOC0i0vVVZ6qnGQOSnkd1w7ksIr5Qh++XtF9EbJG0H/BA07H1czyrAZYvX954YzIznh0F\nmKF6EsrEDWcecNHEDQdYFxFrqG44l9Y3nIeoOtnU+03ccLYzSzccs3Hi3Gg2BH3mxgkRcQ1wzaTY\nWR3vnwDenhx7DlVhg+zcf0P1eNqM9VJlRVRJ/M6I+HjHpomRto/V//1SPw0pzxNJPJvBvr053LXL\n9Pwk/vdJ/BXTPI8N3Lyp7jrd+8htv+GYjYtyc2OSi/huEt+WxLPqXUmVlbXJ7o/dOr3TAxydVVO5\nP4nv1hz+8T83x7NUuksS/6kk7gpnz+ozN46CXkbIjwTeBXxT0m117ENUN5urJJ1C9S/xHbPTRLNS\niKn/SY7+TcdsTDg3mg1FGbmxlyorN1L9aTQ5ZrDNMSvZTkz924gnh9EQM5uCc6PZsJSRG1113qxV\n/E/SzMxsR+OfG8f/Cs1GRi+jAGZmZiUpIze6Q27WGr08J2dmZlaSMnLj+F/h2EhmtmeRFLTRAAAg\nAElEQVTFWgB2zTZms+SzuP+aDIfIp+GbmbVBVgksWz187+bwyjua48cnp/l3SfxV2XIqhyVx6J44\nm1zXHN51n+b4Mdlo7uYkvjKJJ9VdilNGbnRPy6w1yhgFMDMz610ZuXH8r9BsZJTxnJyZmVnvysiN\n7pCbtYr/SZqZme1o/HPj+F+h2cgoYxTAzMysd2XkRnfIzVqjjOfkzMzMeldGbhz/K2ytbJb3xiT+\njeZwMlEdgCPXNscf/lFzfK9tXU5ms0+UMApgZuMoq76yb3P43ye7z0/iu2ffm3VjsqphAD9I4knO\nfOB7zfF9sgov2X38qCR+8jTPU5oycqM75GatUcYogJmZWe/KyI3jf4VmI6OM5+TMzMx6V0ZudIfc\nrDXKGAUwMzPrXRm5MXsAysyGbmI1sm6vKc4grZC0QdJGSWc0bN9F0pX19rWSlnRsO7OOb5B0XB07\nQNINku6QtF7SewdwoWZmZj3qPzdC+/Pj+P/IYTYy+hsFkDQPuAA4lmqN5pslrYmIzqm/pwAPR8TB\nklYB5wInSloGrAIOAfYH/lrSy6lmRn0gIm6V9CLgFklfnXROMzOzWdL/CPko5EePkJu1xsRzct1e\nXR0ObIyIuyLiKeAKYOWkfVYCl9TvrwaOkaQ6fkVEPBkRd1OV+zk8IrZExK0AEfEYcCewqK/LNDMz\n61nfuRFGID96hLx1stJRiS922bYkKW+46AXJAQum9902C6b8J7lA0rqOz6sjYnX9fhFwT8e2zcDr\nJx3/zD4RsV3SI8Dedfzrk47d4cZS//ruNaS1wcxs/O2ZxH8liX+hOfyrWR56ZxL/+ySe3TOzdgI8\nmMQXN4f3ycokZqUVD07i70/ixyRxe1ZfuRFGID+6Q27WGj3NJN8WEcuH0JgdSNoN+Dzwvoh4dNjf\nb2ZmpWpvboTB5Uc/smLWGhPPyXV7dXUvcEDH58V1rHEfSTsDe1ANF6XHSnoe1c3msohIhrvMzMxm\nQ9+5EUYgP7pDbtYaE6uRzfg5uZuBpZIOlDSfahLKmkn7rOHZZeFOAK6PiKjjq+pZ5gcCS4Gb6ufn\nLgTujIiP93mBZmZm09R3boQRyI9+ZMWsNfqbSV4/83Y6cC0wD7goItZLOhtYFxFrqG4el0raCDxE\ndVOi3u8q4A6qByNPi4inJR0FvAv4pqTb6q/6UERcM+OGmpmZ9az/KiujkB/dITdrjf5XI6tvBNdM\nip3V8f4J4O3JsecA50yK3Uh1NzQzM5sDg1mps+35ccoOuaQDgM8C+wJBNXP1fEkfBd4NbK139ajZ\ntGQVTd40vf3f0eUrFjX+vQKuS+Iv63Iym31lrEZmNg7KzY1Zx+i4JP50Er8kiWf5KfverJpKt8Vi\nXpjEn0jiuyXxpCoLv5bEj5zm+a1SRm7s5QobC5/X286LiD+cveaZlWTiOTkzGwHOjWZDUUZunLJD\nHhFbgC31+8ckeWEQs1khqkfbzKztnBvNhqWM3DitKisNhc9Pl3S7pIsk7ZUcc6qkdZLWbd26tWkX\nMwMGtBqZmQ2Zc6PZbCojN/bcIW8ofP5p4CDgMKpRgj9qOi4iVkfE8ohYvnDhwgE02Wyc9V1r1cyG\nyLnRbBjGPzf2dBVNhc8j4v6O7X8G/MWstNCsGIOZSW5mw+HcaDYMZeTGXqqsNBY+l7Rf/QwdwNuA\nb81OE8dVNjP8nUl8U3P48JO6fEf2v/f9SfzELuey2VfGTHKzcVBubszuUUck8cOSeFYi7DtJ/Adp\ni5rN5F6aHZNNDTg4iWdV1Hx/n5kycmMvV3gkDYXPgZMkHUZV7mkT8Fuz0kKzYpQxk9xsTDg3mg1F\nGbmxlyorWeHzMaqratYGZYwCmI0D50azYSkjN47/FZqNjDKekzMzM+tdGbnRHXKz1ihjFMDMzKx3\nZeTG8b9Cs5FRxnNyZmZmvSsjN7pDPmd2S+KvT+L7JvGXdPmObNurZ3Aum31ljAKYWUmyjtSh04xb\nucrIjeN/hWYjQ8Auc90IMzOzFikjN7pDbtYaZYwCmJmZ9a6M3LjTXDfAzCZMzCTv9upO0gpJGyRt\nlHRGw/ZdJF1Zb18raUnHtjPr+AZJx/V6TjMzs9nTf26E9udHd8jNWmNiFKDbq8vR0jzgAuB4YBnV\nAiXLJu12CvBwRBwMnAecWx+7DFgFHAKsAD4laV6P5zQzM5sl/eVGGI386A65WWtMzCSf8SjA4cDG\niLgrIp4CrgBWTtpnJXBJ/f5q4Jh6CfCVwBUR8WRE3A1srM/XyznNzMxmSd+5EUYgPw71oZxbbrll\nm6Tv1h8XANuG+f0tUNo1j+r1vmwuvvSWW269Vnregil2e76kdR2fV0fE6vr9IuCejm2beW7Znmf2\niYjtkh4B9q7jX5907KL6/VTnNLM+ODf6mkfEqOZGGIH8ONQOeUQsnHgvaV1ELB/m98+10q65tOvt\nV0SsmOs2mNnwOTf6mi1XSm70Iytm4+Ne4ICOz4vrWOM+knYG9gAe7HJsL+c0MzNrs9bnR3fIzcbH\nzcBSSQdKmk81CWXNpH3WACfX708Aro+IqOOr6lnmBwJLgZt6PKeZmVmbtT4/zmVhx9VT7zJ2Srvm\n0q53TtXPvJ0OXAvMAy6KiPWSzgbWRcQa4ELgUkkbgYeobiDU+10F3AFsB06LiKcBms457GszK0iJ\n901fs82qUciPqjr/ZmZmZmY2F/zIipmZmZnZHHKH3MzMzMxsDg29Q17KMtySLpL0gKRvdcReLOmr\nkv6l/u9ec9nGQZJ0gKQbJN0hab2k99bxsb1mM7NBKiE/Ojc6N1qzoXbIC1uG+2KqJVY7nQFcFxFL\ngevqz+NiO/CBiFgGHAGcVv+/HedrNjMbiILy48U4Nzo32nMMe4S8mGW4I+JrVLN0O3Uuy3oJ8Nah\nNmoWRcSWiLi1fv8YcCfVSlZje81mZgNURH50bnRutGbD7pA3LV26KNl3HO0bEVvq9/cB+85lY2aL\npCXAa4C1FHLNZmZ9Kjk/FpEnnButG0/qnCN1sfmxqzkpaTfg88D7IuLRzm3jes1mZjYY45onnBtt\nKsPukJe+DPf9kvYDqP/7wBy3Z6AkPY/qhnNZRHyhDo/1NZuZDUjJ+XGs84Rzo/Vi2B3y0pfh7lyW\n9WTgS3PYloGSJKpVru6MiI93bBrbazYzG6CS8+PY5gnnRuvV0FfqlPRLwCd4dpnRc4bagCGRdDlw\nNLAAuB/4CPB/gKuAlwLfBd4REZMnt4wkSUcBfwd8E/hJHf4Q1bNyY3nNZmaDVEJ+dG4EnButwdA7\n5GZmZmZm9ixP6jQzMzMzm0PukJuZmZmZzSF3yM3MzMzM5pA75GZmZmZmc8gdcjMzMzOzOeQOuZmZ\nmZnZHHKH3MzMzMxsDrlDbmZmZmY2h9whNzMzMzObQ+6Qm5mZmZnNIXfIzczMzMzmkDvkZmZmZmZz\nyB3ylpH0J5I+POh9zczMRpVzo407RcRct6EYkjYB+wLbgaeBO4DPAqsj4id9nvto4HMRsXiax70W\n+ATwWuBx4L9HxPn9tMXMzKxXbcuNkr4M/NuO0HxgQ0T8VD9tMevGI+TD98sR8SLgZcDHgP8KXDgX\nDZG0APgK8KfA3sDBwF/NRVvMzKxorcmNEXF8ROw28QL+Afjfc9EWK4c75HMkIh6JiDXAicDJkg4F\nkHSxpN+f2E/SByVtkfR9Sb8pKSQd3Lmv/n/27j7Orqq8+//nS0LCk4CQgCYBEyVQA62oaaRCFY1I\nsGr0LshgVX6Wiq0gPrUW7C1YXk0V6w1iQe9GQR5UQkzx52gDKA+tpUJIQBQCRMcQIeEpITxLwMTr\n/mPvE04Oe505c/aZmXNmf9+v13nlzLXX3mdtSOZas2bta0k7A1cCUyQ9lb+mtNCNTwJXR8S3I+LZ\niHgyIu7q/N2amZkNrkty41aSppPNll/SmTs0K+YB+SiLiJuBtWz76zEAJM0jGzS/hWz2+vDENZ4G\njgLur/up/n5Jh0l6rMnHHwJslPRTSQ9L+oGkfUvekpmZWSmjnBvrfQD474hYM/S7MGudB+Td4X5g\nj4L4e4BvRsTKiPgt8LmhXDQiboiI3Zs0mQYcD3wM2Be4B7hsKJ9hZmY2TEYrN9b7AHDRUK5v1g4P\nyLvDVGBjQXwKcF/d1/cVtCnjGeB7EbE8IjYB/wi8XtJuHf4cMzOzoRqt3AiApMOAlwBLhuP6ZvU8\nIB9lkv6Y7JvODQWHHyCbxa7Zp8ml2imX84uG81xyx8zMRt0o58aa44ErIuKpEtcwa4kH5KNE0q6S\n3g4sIivJdHtBs8XAByW9UtJOQLO6qg8Bew5xdvubwLslHSxp+/z6N0TE40O4hpmZWUd0SW5E0o5k\nS2MuGsp5Zu3ygHzk/UDSk2S/YvsH4Gzgg0UNI+JK4CvA9cAAcFN+6NmCtneTrf9eLekxSVMk/amk\n5E/2EXEd8BngP4CHyR6OeW+7N2ZmZtamrsmNuXcBj+WfYTbsvDFQD5H0SuAOYGJEbB7t/piZmY02\n50YbCzxD3uUkvVvSREkvBs4CfuBvOGZmVmXOjTbWeEDe/T5Mtpzk12RbCv/N6HbHzMxs1Dk32pji\nJStmZmZmZqPIM+RmZmZmZqNofJmT8+1rzwXGAd+IiC80az9p0qSYPn16mY80G3Zr1qxhw4YNGunP\nHS8N+vuq38PVETFvRDpkZm0bSn50brRe4Nw4vNoekEsaB5wPHAGsBZZL6o+IO1PnTJ8+nRUrVrT7\nkWYjYvbs2aPyuQHsPEibJ2HSSPTFzNo31Pzo3Gi9wLlxeJVZsjIHGIiI1RHxHFkR//md6ZZZ9QjY\nfpCXmfUE50ezDqlKbiwzIJ9KVsC/Zm0e24akEyWtkLRi/fr1JT7ObGwT2e+2m73MrCcMmh+dG81a\nU5XcOOwPdUbEwoiYHRGzJ0+ePNwfZ9azqjILYGbOjWatqkpuLPNQ5zpgn7qvp+UxK2XNEOPLmlzr\noUR8IBF/PBHfLRE/KBF/YyJ+QCI+PRGvnrHyk75ZxTk/mnVQFXJjmQH5cmCmpBlk32j6gPd2pFdm\nFVSbBTCznuf8aNYhVcmNbS9ZybeoPRm4GrgLWBwRKzvVMbOqqco6ObOxzvnRrHM6lRslzZO0StKA\npFMLjk+UdHl+fJmk6Xl8T0nXS3pK0nl17XeS9B+S7pa0UlLT0t+DKVWHPCKWAkvLXMPMMlWZBTCr\nAudHs87oRG5ssRTpCcCjEbGfpD7gLOBYYBPwWbJ1uo1rdb8UEddLmgBcK+moiLiynT56p06zLuEZ\ncjMzs211KDe2Uop0PnBx/n4JMFeSIuLpiLiBbGC+VUT8NiKuz98/B9xK9rxIWzwgN+sSVXmS3MzM\nrFUt5sZJtTKi+evEhsu0Uqp7a5t82dnjwJ4t9VHaHXgHcG2r99Wo1JIVK2NNIn58In53cfjSh9Mf\nsSkRX52Ip0Z8ExLxnX5QHH/d54vjh85KXOiCRPyQRHzsKjsLPth23ZImApcArwUeAY6NiDX5sdPI\nfmW3BTglIq7O458A/opsw7TbgQ9GROpvl5mZWUe1kBs3RMSobCUqaTxwGfCViEiNsAblGXKzLlF2\nhrxujdxRwCzgOEmNPwVtXSMHnEO2Ro68XR9wIDAP+KqkcZKmAqcAsyPiILLvi30lb9XMzKwlHfrt\ncSulSLe2yQfZu5FNXA1mIfCriPhya10p5gG5WZcQ2T/IZq9BtL1GLo8viohnI+IesmL1c/J244Ed\n829QOwH3t3mLZmZmQ9KB3Ah1pUjzBzD7gP6GNv08v0zhaOC6iIimfZP+iWzg/vHWupHmJStmXUKk\nVwfVmSRpRd3XCyNiYf6+aI3c6xrO32aNnKTaGrmpwE0N506NiBslfQm4F3gG+FFE/KjVezIzMyuj\nxdzYVJ7vaqVIxwEXRsRKSWcCKyKin2z97KWSBoCN1P02WNIaYFdggqR3AW8FngD+gWxN8a3Z3Bbn\nRcQ32umjB+RmXaSFn/RHdJ2cpBeTzZ7PAB4DvivpfRHxrZHqg5mZVVsnlnMUlSKNiNPr3m8Cjkmc\nOz1xWXWga4CXrJh1jdosQLPXIMqskUud+xbgnohYHxG/A64AXj+kGzMzM2tTB3JjT/AM+bBLFaP4\nn+LwEz8pjjcOq2qazZWuT8R/k4j/NhH/X4n4rxPxcxLxr91ZHP/W3MQJixNxgD9rcqw31dbJldDK\ndt21NXI3UrdGTlI/8B1JZwNTgJnAzcDvgUMk7US2ZGUusAIzM7MR0IHc2BM8IDfrEmV3IyuzRi5v\ntxi4E9gMnBQRW4BlkpaQbXiwGfgZ2RPlZmZmw64qu1h7QG7WJWq7kZVRco3cAmBBQfwM4IySXTMz\nMxuyTuTGXuABuVmXqMosgJmZWauqkhs9IDfrIlWYBTAzMxuKKuRGD8jNukRVZgHMzMxaVZXc6AF5\nx6SqqXwgEf/34nBjTYyaf0rE/zLdo2Q1lSMT8dRjzO8oDj+8tji+1+eK408k4rtuTJR3WfT2RIeA\nXZ9JHNghfU6Xq8o6OTMbi1LFl75aHP7lN4vjX0tcJpVi35eIH/qJxAGAMxPxXZqcY6OlKrnRA3Kz\nLlGVWQAzM7NWVSU3ekBu1kWqMAtgZmY2FFXIjR6Qm3WJqswCmJmZtaoqudEDcrMuUZV1cmZmZq2q\nSm70gNysS1RlFsDMzKxVVcmNHpCbdZFUoRszM7OqqkJuLDUgl7QGeBLYAmyOiNmd6FRvStRkeua7\nxfGlxWF2T8TPS8Sb/dj4oyF+xupEPFG1aq9EycXjP1ccvzhVUSr1uZcn4gAf2pA4MK3JSd1tO2BC\nyWtImgecS/Ybvm9ExBcajk8ELgFeCzwCHBsRa/JjpwEnkP17PiUirpZ0ANv+n3g5cHpEfLlkV83G\ntOrlx0cS8cT36l8lml9SHP76xuL4/P9bHN/rK+ckPgD46LWJA69NxCcNsf0rEvGDEvHeLdc7EjqR\nG3tBJ2bI3xQRqdGRmQ1BmVkASeOA84EjgLXAckn9EXFnXbMTgEcjYj9JfcBZwLGSZgF9wIHAFOAa\nSftHxCrg4LrrrwO+V6KbZlXi/GjWAVWYIa/CPZr1BJHNAjR7DWIOMBARqyPiOWARML+hzXzg4vz9\nEmCuJOXxRRHxbETcAwzk16s3F/h1RKS2nDIzM+uoDuTGnlB2QB7AjyTdIunEogaSTpS0QtKK9evX\nl/w4s7FLZP8gm72ASbV/T/mr/t/dVOC+uq/X5jGK2kTEZuBxYM8Wz+0DLmvv7swqp2l+dG40a02L\nubHnlb2PwyLiNcBRwEmS3tDYICIWRsTsiJg9efLkkh9nNnbVniRv9gI21P495a+FI9I3aQLwTiDx\nUISZNWiaH50bzVrTYm4c/DrSPEmrJA1IOrXg+ERJl+fHl0mansf3lHS9pKcknddwzmsl3Z6f85X8\nN85tKTUgj4h1+Z8Pk60rbfwVt5kNwbhBXoNYB+xT9/W0PFbYRtJ4YDeyp7EGO/co4NaIeKjVezGr\nMudHs84pmRvrn7E6CpgFHJc/O1Vv6zNWwDlkz1hBVrXjs8DfFlz6a8CHgJn5a17rd7Wtth/qlLQz\nsF1EPJm/fytwZrvX633/VRz+fKJ5akLkDxLxJYn4SekesTwRvzERPzIR/2Iinngw/OLEj6s7P1Uc\nf/pfEtdv+mPv5mYHe1IHaq0uB2ZKmkE2mO4D3tvQph84nuxvwdHAdRERkvqB70g6m+yhzpnAzXXn\nHYeXq5i1pJr58bbi8DM/KI6/vji8c6Kayk8Sn7rXKxMH/i4RB1j1i+L4yxLxVYnrvCoR/1+J+NSr\nEgdSydegY3XItz5jBSCp9oxVfdGD+cDn8vdLgPMkKSKeBm6QtN82/ZJeCuwaETflX18CvAu4sp0O\nlqmysjfwvXx2fjzwnYhI/W0zs0GU3Y0sIjZLOhm4Or/UhRGxUtKZwIqI6AcuAC6VNABsJBu0k7db\nTPbNaTNwUkRsga2DiyOAD5fonlmVOD+adUiLuXGSpBV1Xy9sWNJZ9JzU6xqusc0zVpJqz1ilKiVN\nza9Tf83GZ69a1vaAPP8pI/XzoZkNUSdmASJiKQ1V7iPi9Lr3m4BjEucuABYUxJ8m+6ZkZi1wfjTr\nnBZz44Zer/XvnTrNukTZGXIzM7OxpkO5cSjPWK1teMaq2TXrdyMsumbLxkq1GLOe16knyc3MzMaK\nDuXGrc9Y5VXD+sieqapXe8YK6p6xSl0wIh4AnpB0SF5d5QPA91vrzgt5htysi3iG3MzMbFtlc2OZ\nZ6wAJK0BdgUmSHoX8NZ8F+yPABcBO5I9zNnWA53gAXkHJaqsJJ4Y5/5EfGYi/kQivke6R1yTiH8g\nEZ+bqMr1y5uL4x9NXOfjxeGnGwsM1bwoEW+6AnPs/dXt0JPkZmajIFGK5MlE8732Kgw/fdjDxe33\nKQ6TyivvTMQhWSGMLYn4s4n4nYn4pkT871KTp43PFtbsnohXS6dyY8lnrKYn4iuAgzrQvTE4qjHr\nUV5DbmZmtq2q5EYPyM26hGfIzczMtlWV3OgBuVkX8VPWZmZm26pCbvSA3KxLCJgw2p0wMzPrIlXJ\njR6Qm3UJUY1ZADMzs1ZVJTd6QD5kqaekzy0O355o/s/F4UcPK46nHlTfN/X0N8D0RHzuEYkDVxSH\n97+tOH713xfH7/lpcTx1E3+0a+LA6Yk4jMWnz6syC2BmY9Gvi8M7pdonhh+LE80fS8RTlU5WJOIA\n6xPx1cXhbTZgr6NEnCWJ+PFfK47vdWTihPmJeLVUJTd6QG7WRaowC2BmZjYUVciNVbhHs57Qid3I\nJM2TtErSgKRTC45PlHR5fnyZpOl1x07L46skHVkX313SEkl3S7pL0p+UulEzM7MWVWUXa8+Qm3WJ\nsrVWJY0DzgeOANYCyyX157uJ1ZwAPBoR+0nqA84CjpU0i2xXsgOBKcA1kvaPiC1k67Guioij8y2H\nk7+ENjMz66Sq1CH3DLlZl+jALMAcYCAiVkfEc8AiXrgIcT5wcf5+CTBXkvL4ooh4NiLuAQaAOZJ2\nA95AtqUwEfFcRKRWc5qZmXVUVWbIPSA36xK1WYBmL2CSpBV1rxPrLjEVuK/u67V5jKI2EbEZeBzY\ns8m5M8gegfqmpJ9J+oaknUvfrJmZWQtazI09z0tWhuyLxeEf/7443jgcqvlhcfiORPM/nZY48KVE\nHOCU1IFjE/FdEvFDEvHLi8MzBhLtf5OIT0rED0zEAXZocqw3tbgb2YaImD3snXneeOA1wEcjYpmk\nc4FTgc+OYB/MrOs9WBzeZa9E+0RlkR2+WRyfnrhMqkjXmkQcYI9E/DXFYaUW6aV+V/i3iXgybaVy\npkF1dur0DLlZFyk5C7AO2Kfu62l5rLCNpPHAbsAjTc5dC6yNiGV5fAnJtGVmZtZ5VZgh94DcrEt0\nYJ3ccmCmpBn5w5d9QH9Dm37g+Pz90cB1ERF5vC+vwjIDmAncHBEPAvdJOiA/Zy7pyr9mZmYdVZU1\n5F6yYtYlyj5JHhGbJZ0MXJ1f6sKIWCnpTGBFRPSTPZx5qaQBYCPZoJ283WKywfZm4KS8wgrAR4Fv\n54P81cAHS3TTzMysZVWpsuIBuVmX6MQ6uYhYCixtiJ1e934TcEzi3AXAgoL4bcBIrls3MzMDqrOG\n3ANysy5RlVkAMzOzVlUlNw46IJd0IfB24OGIOCiP7UFWYmM62bPM74mIR4evm13kxz8tjqdW1b47\nEf98cfhPU4VOTk7Ev5WIQ7qYSurp9qTUX5NU6ZdU3JqpyiyA2Vjh/Fhn8y+L4+OnJE5YWRzeUhxm\nx+JSJ49M+21hfM+TEtcBOC9VZuUlxeEpiQT/34nLvDoRT1ZZeSp1wOhcbpQ0j2yju3HANyLiCw3H\nJwKXAK8lK3ZwbESsyY+dRrax3hbglIi4Oo9/AvgrIIDbgQ/mv4keslYe6rwImNcQOxW4NiJmAtfm\nX5tZSdsN8jKzrnIRzo9mw65sbqzbyfooYBZwXL5Ddb2tO1kD55DtZE3DTtbzgK9KGidpKlmB6dn5\nD+Tj8nZt32NTEfETsoe/6tXv9ncx8K52O2BmGQETBnmZWfdwfjQbfh3KjR3fyTpvNx7YMS8jvBNw\nfxu3CLQ/6bZ3RDyQv38Q2LvdDphZHU+Rm/U650ezThs8NzbbxRqGYSfriFhHtj3jvcADwOMR8aN2\nb7H0Q50REZIidTz/j3IiwL777lv248zGrto0QDNtrUwzs9HQLD86N5q1qLXcONK7WCPpxWSz5zPI\n9m39rqT3RUSzp/uS2p1ze0jSS/MOvRR4ONUwIhZGxOyImD158uQ2P86sAoRnyM16X0v50bnRrEWd\nyY3DsZP1W4B7ImJ9RPwOuAJ4fes3tq12Z8hru/19If/z++12oHs9Vhy+LdF890Q89RcltcroHYl4\n6peen0jEAXY8LXHAVVC6UiuzAMUFBcyse1QgPxZIpEw2JpLd/on2VybiOyWqqbwh0f7BRBzg3xuX\n/dck4m9LNL8vEZ+0V+JAKvful4gb0KncuHUna7LBdB/w3oY2tX+7N1K3k7WkfuA7ks4GppDvZA38\nHjhE0k7AM2Q7Wa9o8a5eoJWyh5cBh5Otz1kLnEH2jWaxpBOA3wDvabcDZlbHs+BmPcP50WyElMyN\nw7ST9TJJS4Bb8/jPgIXt9nHQAXlEHJc4NLfdDzWzAi5EbtZTnB/NRkCHcuMw7WR9BtkP4qV5Ps6s\nW3SgtpOkeZJWSRqQ9IL6x5ImSro8P75M0vS6Y6fl8VWSjqyLr5F0u6TbJLX96zgzM7Mhq0hN4NJV\nVsysQ2oPrrR7+vMbHxxBVpZpuaT+iKjfZm7rxgeS+sg2Pji2YeODKcA1kvbPfy0H8KaI2NB+78zM\nzNpQMjf2igrcolmPKD8LMFwbH5iZmY0Oz5BX3UBxeHWieWN5+ZpVifhOifjbE/Glifg/JuIA7JyI\nN9bLzz319eL4uMRldkyN185KxA9PxG2rwX9EntSwbGRhRNQeIinavOB1Dedvs+15aDoAACAASURB\nVPGBpPqND25qOLf2tzqAH+X1lP+t7vPMzDKpQdGvEvHJieorH0+0fzoRPyUR/4NEHODNifiLE2XO\nbvpBcbzxu2vNM4lK0Du+KnHCKxJx26oC08cekJt1i9YeXBnxzQ+AwyJinaS9gB9LujvfMtzMzGx4\nVaTgQQV+5jDrESL7bUSzV3PDsfEB+fbARMTDwPfwUhYzMxsp5XNjT/CA3Kxb1GYBmr2a27rxgaQJ\nZA9p9je0qW18AHUbH+TxvrwKywzyjQ8k7SzpRQCSdgbeCtxR5jbNzMxaVj439gQvWTHrFrVZgDYN\nx8YHkvYGvpc998l44DsRcVX7vTQzMxuCkrmxV3hAbtYtOrBOrtMbH0TEaiD1JJKZmdnwqsgacg/I\nkxqX3uZen2j+lkT8pER8ZiKeepI81f4VL08cAFhZHH7isuL4xcXhZKWY+28ujl/ypuL4Ll9KXOhT\niXgFVWAWwMzGoF33KI6/aGNx/L7i8KWJ5u+flPjcP0zEX5aIA7w4VeZsWnH4wUTzQ/Yvjt/0y0T7\n1E3sl4jbVhXIjR6Qm3WLiswCmJmZtawiudEDcrNuUZF1cmZmZi2rSG70gNysW1RkFsDMzKxlFcmN\nHpCbdZMKzAKYmZkNSQVyowfkZt1iOyoxC2BmZtayiuRGD8iTNhSHJyeav3Tf4vi6e4vjfzDE7qQ+\nl7VNTko80b1r4snwTYknw19THH7i/MTlX5nozrf/tjj+BldZ2cpbdZlZT/rn4vAbPlkc//VvC8Pv\n/17i8usT8ccS8VRFNADeWByOrxXHhzoYTBScgdcl4qnqK7ZVBXKjB+Rm3ULAhNHuhJmZWRepSG70\ngNysW4hKzAKYmZm1rCK5sQK3aNYjarMAzV5mZmZV0qHcKGmepFWSBiSdWnB8oqTL8+PLJE2vO3Za\nHl8l6ci6+O6Slki6W9Jdkv6k3dv0DLlZN/GPyGZmZtsqmRsljQPOB44ge/huuaT+iLizrtkJwKMR\nsZ+kPuAs4FhJs4A+4EBgCnCNpP0jYgtwLnBVRBwtaQKQ2gZ2UE7/Zt2iVmu12WuwSwzDDEB+bJyk\nn0n6Yfs3aGZmNkQdyI3AHGAgIlZHxHPAImB+Q5v5wMX5+yXAXEnK44si4tmIuAcYAOZI2g14A3AB\nQEQ8FxGpx4wH5QG5Wbeo7UbW7NXs9OdnAI4CZgHH5T/Z19s6AwCcQzYDQMMMwDzgq/n1aj4G3NX+\nzZmZmbWhtdw4SdKKuteJDVeZCtxX9/XaPFbYJiI2A48DezY5dwZZ/Z9v5hNW35C0c7u3OeiSFUkX\nAm8HHo6Ig/LY54AP8Xwhos9ExNJ2O9GdNhWHk4OixH/KlySap8oYJj6WVanPbfwBr87m7xbHf5Vo\nn+rrJcXhF6U+9+hE/MpE/A2pC1VM+d3Its4AAEiqzQDU/0puPvC5/P0S4LzGGQDgHkkD+fVulDQN\n+DNgAZCoYWZWPdXNj0UOTcSPLw7vkygx+GDiMlsS8URZXvZJxAGYNrTP/n3qOjsMKdzkgDXTWm7c\nEBGzh78z2xhP9jfwoxGxTNK5wKnAZ9u5WCsz5BeRzZg1OiciDs5fFfhmYzYCys0CDMcMAMCXgU/T\nJC2ZVdRFOD+aDb8Svz3OrWPbH9Om5bHCNpLGA7sBjzQ5dy2wNiKW5fElpH9EHNSgA/KI+Amwsd0P\nMLMWtbZObkNEzK57LRzWLkm12b9bhvNzzHqR86PZCOjMGvLlwExJM/KHL/uA/oY2/Tz/K52jgesi\nIvJ4X/4M1gxgJnBzRDwI3CfpgPycuWz7G+khKbOG/GRJv5B0oaQXpxpJOrE2m7d+fWqrLTMru4ac\n4ZkBOBR4p6Q1ZA/BvFnSt4Z0X2bVM2h+dG40a1H53Fj7jfDJwNVkz0MtjoiVks6U9M682QXAnvmS\nzU+SLT8hIlYCi8kG21cBJ+UVVgA+Cnxb0i+Ag0luWTu4dgfkXwNekX/4A8D/STWMiIW12bzJk5P7\nv5tZ+VmA4ZgBOC0ipkXE9Px610XE+0rdp9nY1lJ+dG40a1FnZsiJiKURsX9EvCIiFuSx0yOiP3+/\nKSKOiYj9ImJO7Xms/NiC/LwDIuLKuvht+b/jP4qId0XEo+3eZlt1yCPiodp7SV8HXArNrKzaLECb\nImKzpNoMwDjgwtoMALAi/6ZzAXBpPgOwkWyQTd6uNgOwmW1nAMysRc6PZh1WMjf2irYG5JJeGhEP\n5F++G7ijc13qcqkqKKwpDqd+E/nHiXjqL93rU5+7MnVg20f06r3yuER8enH85Z8vDCv1+5WfJ+J7\nJOKWKV9lhfwBsqUNsdPr3m8Cjkmcu4Cskkrq2v8J/Ge5HpqNbdXNjwcl4l8sDk/4dHH80FSpk8WJ\n+EAi/utEHCDxSMzvEs3fmIg/84viuKcyOqsDubEXtFL28DLgcLLqDmuBM4DDJR0MBNlI9MPD2Eez\n6qjALIDZWOH8aDZCKpAbBx2QR0TRdOoFw9AXs2qryCyA2Vjh/Gg2AiqSG9tasmJmw0B471wzM7N6\nFcmNHpCbdQsBE0a7E2ZmZl2kIrnRA3KzblKBWQAzM7MhqUBu9IA8aXNx+LpE8wOKdxV/7sbi5hNe\nlbjO/941cSDxBPs9P020B2YckTjw/UT87PS1irw8EU+V1N1haJevnIqskzOzKtlliPGnEvG7E/Gr\nE/Ejkz1i3ZXF8dT3310To8GnivM+M96UuNCkdJ8srSK50QNys25RkVqrZmZmLatIbvSA3KxbVGQW\nwMzMrGUVyY0ekJt1i4rMApiZmbWsIrnRA3KzblGRWQAzM7OWVSQ3VuC5VbMeMm6Q1yAkzZO0StKA\npFMLjk+UdHl+fJmk6XXHTsvjqyQdmcd2kHSzpJ9LWinpHztwl2ZmZq0rmRt7gWfIk15XHE5VFvl8\ncXjCkkT7tanPTT15nvhfNWOn1IXgph8Xxz+SaH/rF4vjqaIsb0zENybiqcoylik5CyBpHHA+cATZ\n37Dlkvoj4s66ZicAj0bEfpL6gLOAYyXNAvqAA4EpwDWS9geeBd4cEU9J2h64QdKVEXFT+z01M9uQ\niCeqpjyQqIySKHTC1JXpj74tEX9zIn5t4kMSw4R0NZW9k12yJjxDbmYjqrZOrv1ZgDnAQESsjojn\ngEXA/IY284GL8/dLgLmSlMcXRcSzEXEPMADMiUytDtn2+SvavkczM7OhKJ8be4IH5GbdojYL0OwF\nkyStqHudWHeFqcB9dV+vzWMUtYmIzcDjwJ7NzpU0TtJtwMPAjyNiWel7NTMza0VrubHnecmKWTcZ\n/Cf9DRExewR6slVEbAEOlrQ78D1JB0XEHSPZBzMzq7AxMgvejGfIzbpF+VmAdcA+dV9Py2OFbSSN\nB3YDHmnl3Ih4DLgemNfaDZmZmZXUoRnyThc9qDs2TtLPJP2wvRvMeEBu1i1E9i+y2au55cBMSTMk\nTSB7SLO/oU0/cHz+/mjguoiIPN6Xf0OaAcwEbpY0OZ8ZR9KOZA+MpvawNjMz66zyubG+6MFRwCzg\nuLyYQb2tRQ+Ac8iKHtBQ9GAe8NX8ejUfA+5q7+ae5yUrSdOLw29LNP+vRHxVIv761Oem/pfsnIin\nnuYGlt9bHP9Jov3bVxfHUyuGG1cn17woET90r8QBA7JvOhPaPz0iNks6maxMwTjgwohYKelMYEVE\n9AMXAJdKGiCrh9OXn7tS0mLgTmAzcFJEbJH0UuDi/JvPdsDiiCg1C2Bmlk6aiSorv000f8UexfHv\nJPIfwJZEfMfEyO7JRJWVVJ92+YPEgf3SfbK0krkxt7XoAYCkWtGD+ipk84HP5e+XAOc1Fj0A7snz\n5xzgRknTgD8DFgCfLNNBD8jNuknJ31lFxFJgaUPs9Lr3m4BjEucuIPumUh/7BfDqcr0yMzMrofx6\njqLCBY2FK7cpeiCpvujBTQ3n1qYkvwx8mvRUZMs8IDfrFtvRiVkAMzOzsaO13DhJ0oq6rxdGxMJh\n6xMg6e3AwxFxi6TDy17PA3KzbuKnOszMzLY1eG4crALZUIoerG2x6ME7gXdKehuwA7CrpG9FxPsG\n7W0Bp3+zblGRWqtmZmYt60xu7HjRg4g4LSKmRcT0/HrXtTsYB8+Qm3WP2m5kZmZmlulAbhyOogfl\nevRCgw7IJe0DXALsTbZl9sKIOFfSHsDlZOVI1gDviYhHO93B0fOS4vArFhXH/6qvOP6txOXvT8Q3\nJZ4Mn7dDcfyBJk+SvyoRTzyi99WB4vhHTkpc5+JE/KJUh85OHTB4fhbAzLpedXPjUKWqpCYKUqRy\nWup74w82FsebDZf+MBG/KlFNJVUVba/liQMjunfb2Neh3NjpogcNx/8T+M8y/Wtlycpm4FMRMQs4\nBDgpr8l4KnBtRMwErs2/NrMyxg3yMrNu4dxoNlIqkBsHHZBHxAMRcWv+/kmy4udTyeoy1uZILwbe\nNVydNKsEryE36xnOjWYjpCK5cUhryPNtRF9NtlXM3hHxQH7oQbJf2xWdcyJwIsC+++7bbj/Nxj6v\nITfrSc6NZsOoIrmx5SorknYB/h34eEQ8UX8sfwo1is6LiIURMTsiZk+ePLlUZ83GtIrMApiNJc6N\nZsOsIrmxpQG5pO3JvuF8OyKuyMMP5dtqk//58PB00awiarMAY3ydnNlY4dxoNgIqkhtbqbIislIw\nd0VEfZmMWr3GL+R/fn9Yeth15haH5+1VHF+f+F78d4nLL0vEf/7LxPUT7QGuKQ5/KVFN5c7UdVIV\nYX6XiP/R/okDif929rxxg31n6XilJTNrg3Njo02J+C3F4WcS1VRWJy6TqhqWypl7JOLNjj2XiO/1\n8sSB/Zp8iHVUBXJjKzPkhwLvB94s6bb89TaybzZHSPoV8Jb8azNrm8h+Rm72GuQK0jxJqyQNSHpB\ndYd8Y4PL8+PL8rWvtWOn5fFVko7MY/tIul7SnZJWSvpYB27UbCxwbjQbEeVzYy8Y9C4i4gay/xpF\nPOVp1jHbke2+28yzySOSxgHnA0cAa4Hlkvojov6XHycAj0bEfpL6gLOAY/NybX3AgcAU4BpJ+/N8\nabdbJb0IuEXSjxuuaVY5zo1mI6VcbuwVLT/UaWYjodQswBxgICJWR8RzwCKyEmz16kuyLQHm5r96\nnw8siohnI+IeYACY06S0m5mZ2QjxDLmZjZhWZgGYJGlF3dcLI2Jh/n4qcF/dsbXA6xrO39om30r4\ncWDPPH5Tw7nbDLwbSruZmZmNgJZyY8/zgNysa9TWyTW1ISJGfF/mZqXdzMzMhk9LubHnjf077LhJ\nifgpxeGX/e/i+D8kLrNPIn5+Iv5YIg7ZSuACf5t6YPy/hvgZX0598L8l4i9JnWBA9k1nYpkLrGPb\nv0HT8lhRm7WSxgO7AY80OzdR2s3MrE4qSS0uDp+XaP7hRPwDifiDifi/JuIAP0/E3zEhceCfE/Hd\nm3yIdU7p3NgTvIbcrGuUfpJ8OTBT0gxJE8ge0uxvaFMryQZwNHBdvnlJP9CXV2GZAcwEbm5S2s3M\nzGwEuMqKmY2ocuvk8jXhJwNXk22VcGFErJR0JrAiIvrJBteXShoANpIN2snbLSYrR78ZOCkitkg6\njKy02+2Sbss/6jMRsbTtjpqZmbXMa8jNbMSV+yeZD5SXNsROr3u/CTgmce4CYEFDrFlpNzMzsxEw\n9oerY/8OzXpGNWYBzMzMWleN3OgBuVnXqMaT5GZmZq2rRm4c+3c4YhIbs73hi8XxGxKV425IXP5V\nifgjTbp0fyK+PBE/IBFPVYSZ+zeJAwcnu2TNiCrMApjZWNRY0Cn3zM3F8ZmJy/w0EU/lrSMT8VQu\nhRfuzrDV3yfi3nh1dFUjN7rKilnXqMaT5GZmZq3rTG6UNE/SKkkDkk4tOD5R0uX58WX5Zni1Y6fl\n8VWSjsxj+0i6XtKdklZK+liZu3SGN+sa1VgnZ2Zm1rryuVHSOLJi+UeQ7US9XFJ/RNxZ1+wE4NGI\n2E9SH3AWcKykWWQVyQ4k2+HlGkn7k1Uk+1RE3CrpRcAtkn7ccM2WeYbcrGt4htzMzGxbHcmNc4CB\niFgdEc8Bi4D5DW3mAxfn75cAc/O9OOYDiyLi2Yi4BxgA5kTEAxFxK0BEPAncBUxt9y6d4c26RjV2\nIzMzM2tdS7lxkqQVdV8vjIiFdV9PBe6r+3otL3yaYGubfF+Px4E98/hNDeduM/DOl7e8Glg2WEdT\nPCA36xrVeJLczMysdS3lxg0RMXsEOvMCknYB/h34eEQkKnYMztnfrGt4DbmZmdm2OpIb1wH71H09\njReWBqq1WStpPLAbWS275LmSticbjH87Iq4o00EPyDsm9YPZ/xSHP/P9RPufDy0ev0x3SbsmDqRq\nPv15Ip4q+TQtEfegsn3+J2lmvejq4vDGRPPpxeFnXl0c3/GwxHXemIg/mIgDHPKGxIGPJOKTmlzM\nRkbp3LgcmClpBtlgug94b0ObfuB44EbgaOC6iAhJ/cB3JJ1N9lDnTODmfH35BcBdEXF22Q76oU6z\nrlGbBWj2aq7TZZ3y+IWSHpZ0R8kbNDMzG6LyuTEiNgMnk/3keBewOCJWSjpT0jvzZhcAe0oaAD4J\nnJqfuxJYDNwJXAWcFBFbgEOB9wNvlnRb/npbu3fp6TizrlFuDflwlHXKv+lcBJwHXNJ258zMzNrS\nmeerImIpsLQhdnrd+03AMYlzFwALGmI35J3rCM+Qm3WN2m5kbc8CdLysE0BE/IT0L57NzMyGUenc\n2BM8Q27WNVqaBWhW2mlYyzqZmZmNvGpUIBv7d2jWM1p6knzUSjuZmZmNvGpUIBt0QC5pH7K1o3sD\nQTYjd66kzwEfAtbnTT+Tr8+pqNR/yoOGGB+ijq1estFXehZgWMo6mdkLOTc2SlTvmpoYSE28tTC8\n426Jy/9LIn5fIv6xRByAUxJxV1PpTp4hr9kMfCoibpX0IuAWST/Oj50TEV8avu6ZVUltnVzbOl7W\nqUxnzMY450azEVE6N/aEQQfkEfEA8ED+/klJd+G1pWbDQMC4ts/O14TXyjqNAy6slXUCVkREP1lZ\np0vzsk4byQbt5O1qZZ0283xZJyRdBhxOtn59LXBGRFzQdkfNxgDnRrORUi439ooh/Q4gr1n8amAZ\nWf3FkyV9AFhBNlPwaME5JwInAuy7774lu2s2lpVfJ9fpsk55/LhSnTIb45wbzYZTNdaQt1z2UNIu\nZNuDfjwingC+BrwCOJhsluD/FJ0XEQsjYnZEzJ48eXIHumw2lo0f5GVm3cS50WwkjP3c2NJdSNqe\n7BvOtyPiCoCIeKju+NeBHw5LD80qoxqzAGZjhXOj2UioRm5spcqKyNad3hURZ9fFX5qvoQN4N+Bt\ntc1KqcaT5GZjgXNjoz9PxJ9KxIurrPD1RPMtifgxeyQOXJiIAxyZiPv7b3eqRm5s5Q4PBd4P3C7p\ntjz2GeA4SQeTlXtaA3x4WHpoVhnVeJLcbIxwbjQbEdXIja1UWbmB4mrXFairajaSqvFrObOxwLnR\nbKRUIzeO/d8BmPWMavxazszMrHXVyI1j/w7NekY1fi1nZmbWumrkRg/IzbpGNWYBzMzMWleN3Dj2\n79CsZ2wHTBztTpiZteG1ifjm4vCk3Yvjx6xMXOeARPwvEvFDE3Gowmzr2FKN3OgBuVlX8T9JMzOz\nbY393Dj279CsZ1TjSXIzM7PWVSM3bjfaHTCzmto6ufa3B5Y0T9IqSQOSTi04PlHS5fnxZZKm1x07\nLY+vknRkq9c0MzMbPuVzI3R/fvSA3Kxr1GYBmr3SJI0DzgeOAmaRbVAyq6HZCcCjEbEfcA5wVn7u\nLKAPOBCYB3xV0rgWr2lmZjZMyuVG6I386AG5WVcpNQswBxiIiNUR8RywCJjf0GY+cHH+fgkwN98C\nfD6wKCKejYh7gIH8eq1c08zMbBiVniHv+vw4omvIb7nllg2SfpN/OQnYMJKf3wWqds+9er8vG40P\nveWWW6+Wtp80SLMdJK2o+3phRCzM308F7qs7thZ4XcP5W9tExGZJjwN75vGbGs6dmr8f7JpmVoJz\nYyv3/NNE/Jud7stI6cX/z72aG6EH8uOIDsgjYnLtvaQVETF7JD9/tFXtnqt2v2VFxLzR7oOZjTzn\nRt+zpVUlN3rJitnYsQ7Yp+7raXmssI2k8cBuwCNNzm3lmmZmZt2s6/OjB+RmY8dyYKakGZImkD2E\n0t/Qph84Pn9/NHBdREQe78ufMp8BzARubvGaZmZm3azr8+No1iFfOHiTMadq91y1+x1V+Zq3k4Gr\ngXHAhRGxUtKZwIqI6AcuAC6VNABsJPsGQt5uMXAn2dZ6J0XEFoCia470vZlVSBW/b/qebVj1Qn5U\nNvg3MzMzM7PR4CUrZmZmZmajyANyMzMzM7NRNOID8qpswy3pQkkPS7qjLraHpB9L+lX+54tHs4+d\nJGkfSddLulPSSkkfy+Nj9p7NzDqpCvnRudG50YqN6IC8YttwX0S2xWq9U4FrI2ImcG3+9VixGfhU\nRMwCDgFOyv/fjuV7NjPriArlx4twbnRutBcY6RnyymzDHRE/IXtKt179tqwXA+8a0U4No4h4ICJu\nzd8/CdxFtpPVmL1nM7MOqkR+dG50brRiIz0gL9q6dGqi7Vi0d0Q8kL9/ENh7NDszXCRNB14NLKMi\n92xmVlKV82Ml8oRzozXjhzpHSV5sfszVnJS0C/DvwMcj4on6Y2P1ns3MrDPGap5wbrTBjPSAvOrb\ncD8k6aUA+Z8Pj3J/OkrS9mTfcL4dEVfk4TF9z2ZmHVLl/Dim84Rzo7VipAfkVd+Gu35b1uOB749i\nXzpKksh2uborIs6uOzRm79nMrIOqnB/HbJ5wbrRWjfhOnZLeBnyZ57cZXTCiHRghki4DDgcmAQ8B\nZwD/P7AY2Bf4DfCeiGh8uKUnSToM+G/gduD3efgzZGvlxuQ9m5l1UhXyo3Mj4NxoBUZ8QG5mZmZm\nZs/zQ51mZmZmZqPIA3IzMzMzs1HkAbmZmZmZ2SjygNzMzMzMbBR5QG5mZmZmNoo8IDczMzMzG0Ue\nkJuZmZmZjSIPyM3MzMzMRpEH5GZmZmZmo8gDcjMzMzOzUeQBuZmZmZnZKPKA3MzMzMxsFHlA3mUk\n/V9Jn+10WzMzs17l3GhjnSJitPtQGZLWAHsDm4EtwJ3AJcDCiPh9yWsfDnwrIqYN4ZyJwLnAu4Ht\ngf8B/joi1pXpi5mZWau6MDfuTpYbj8pDX42Iz5Xph9lgPEM+8t4RES8CXgZ8Afh74IJR6svHgD8B\n/giYAjwK/Oso9cXMzKqrm3LjOcBOwHRgDvB+SR8cpb5YRXhAPkoi4vGI6AeOBY6XdBCApIsk/VOt\nnaRPS3pA0v2S/kpSSNqvvq2knYErgSmSnspfU1roxgzg6oh4KCI2AZcDB3b6Xs3MzFrRJbnxHcAX\nI+K3EbGG7AeDv+zwrZptwwPyURYRNwNrgT9tPCZpHvBJ4C3AfsDhiWs8TfartfsjYpf8db+kwyQ9\n1uTjLwAOlTRF0k7AX5B98zIzMxs1o5wbAdTw/qCh34VZ6zwg7w73A3sUxN8DfDMiVkbEb4HPDeWi\nEXFDROzepMmvgPuAdcATwCuBM4fyGWZmZsNktHLjVcCpkl6Uz7r/JdkSFrNh4wF5d5gKbCyITyEb\nMNfcV9CmjPOBicCewM7AFXiG3MzMusNo5cZTgGfIJq2+D1xGNltvNmw8IB9lkv6Y7JvODQWHHwDq\nnwzfp8ml2imXczBwUURsjIhnyR7onCNpUhvXMjMz64jRzI15TvyLiHhJRBxINla6eajXMRsKD8hH\niaRdJb0dWERWkun2gmaLgQ9KemW+xrtZXdWHgD0l7TaEbiwHPiBpN0nbAx8hW2u3YQjXMDMz64hu\nyI2SXiFpT0njJB0FnAj802DnmZXhAfnI+4GkJ8l+xfYPwNlAYTmliLgS+ApwPTAA3JQferag7d1k\nv1ZbLemx/EHNP5X0VJO+/C2wiezXcuuBt5HVJDczMxtJ3ZQbXwvcDjwJfB74i4hY2d5tmbXGGwP1\nEEmvBO4AJkbE5tHuj5mZ2WhzbrSxwDPkXU7SuyVNlPRi4CzgB/6GY2ZmVebcaGONB+Td78PAw8Cv\nybYU/pvR7Y6Zmdmoc260McVLVszMzMzMRpFnyM3MzMzMRtH4Mifn29eeC4wDvhERX2jWftKkSTF9\n+vQyH2k27NasWcOGDRs0eMvOGi8N+vuq38PVETFvRDpkZm0bSn50brRe4Nw4vNoekEsaR7bT4xFk\nO1gtl9QfEXemzpk+fTorVqxo9yPNRsTs2bNH5XODbLvUZp4Eb9pk1uWGmh+dG60XODcOrzJLVuYA\nAxGxOiKeIyviP78z3TKrHgHbD/Iys57g/GjWIVXJjWUG5FPJCvjXrM1j25B0oqQVklasX7++xMeZ\njW0i+912s5eZ9YRB86Nzo1lrqpIbh/2hzohYGBGzI2L25MmTh/vjzHpWVWYBzMy50axVVcmNZR7q\nXAfsU/f1tDxmZm0aKz/pm1Wc86NZB1UhN5YZkC8HZkqaQfaNpg94b0d6ZVZBtVkAM+t5zo9mHVKV\n3Nj2kpV8i9qTgauBu4DFEbGyUx0zq5pOrJOTNE/SKkkDkk4tOD5R0uX58WWSpufxPSVdL+kpSec1\nnPNaSbfn53xF0oiXvTLrJc6PZp1TlTXkpeqQR8RSYGmH+mJWaWVnAVostXYC8GhE7CepDzgLOBbY\nBHwWOCh/1fsa8CFgGdm/93nAlSW6ajbmOT+adYZnyM1sRHVgFqCVUmvzgYvz90uAuZIUEU9HxA1k\nA/Pn+yS9FNg1Im6KiAAuAd7V1g2amZkNkWfIzWxEdWAWoKjU2utSbSJis6THgT2BDU2uubbhmi8o\nb2pmZjYcqjJD7gG5WRdp4Sf9SZLqt/RbGBELh61DZmZmo2yszII34wG5WZdocRZgQ0Sk9i9updRa\nrc1aSeOB3YBHmnzeuvw6za5pZmY2LKoyQ+415GZdQmT/IJu9BrG11JqkVbo44AAAIABJREFUCWSl\n1vob2vQDx+fvjwauy9eGF4qIB4AnJB2SV1f5APD91u/KzMysfR3Ijdl12qxClh87LY+vknRkXfwT\nklZKukPSZZJ2aPc+PSA36xICJgzyaiZVak3SmZLemTe7ANhT0gDwSWDrNyVJa4Czgf9P0lpJs/JD\nHwG+AQwAv8YVVszMbISUzY2wTRWyo4BZwHF1Oa5maxUy4ByyKmTk7fqAA8mqjH1V0jhJU4FTgNkR\ncRDZypq+du/TS1bMukjZn5CLSq1FxOl17zcBxyTOnZ6Ir+CFpRDNzMxGRAdmj7dWIQOQVKtCVl8W\neD7wufz9EuC8/DfD84FFEfEscE8+oTUHuJdsHL2jpN8BOwH3t9tBz5CbdYlOzAKYmZmNJS3mxkmS\nVtS9Tmy4TFEVssaKYdtUIQNqVcgKz42IdcCXyAbmDwCPR8SP2r1Pz5CPmssT8X9OxO8oDt/y+/RH\nvDwRf3HqwKcT8T9LxKcl4taO2jo5MzMzy7SYG5sVPBgWkl5MNns+A3gM+K6k90XEt9q5nvO/WZeo\nPUne7GVmZlYlHcqNQ6lCRkMVstS5bwHuiYj1EfE74Arg9S3fWAMPyM26RFV2IzMzM2tVh3JjmSpk\n/UBfXoVlBjATuJlsqcohknbK15rPJSuo0BYvWTHrElWptWpmZtaqTuTGfGfqWhWyccCFtSpkwIqI\n6CerQnZp/tDmRvKKKXm7xWQPgG4GToqILcAySUuAW/P4z4C2N+rzgNysi3gW3MzMbFudyI0lq5At\nABYUxM8AzuhA9zwgN+sWniE3MzPbVlVyowfko+bfisP/+Ivi+A+Lw2etSH/C36f+Bs9aXRx/618X\nx1PFVyb9XeLAKYm4q7I0U1snZ2ZmZpmq5EYPyM26RFVmAczMzFpVldzoAblZl6jKNx0zM7NWVSU3\nekBu1kWq8Gs5MzOzoahCbvSA3KxLVGUWwMzMrFVVyY0ekJt1iao8uGJmZtaqquRGD8jNukRVZgHM\nzMxaVZXcWGpALmkN8CSwBdgcEbM70alquGVIre9NlDfc2OykPRLxLyfi1yXilyTix/1Lcfylr0ic\n8OeJ+KREvFqqMgtgVgXOj522JhHfkIj/vMm1Uuf8eojtH0rEH0zEd0nE907E5yfib0zEAQ5qcqw3\nVSU3dmKG/E0RkfrbamYtqsosgFmFOD+alVSV3LjdaHfAzJ43bpCXmZlZ1XQiN0qaJ2mVpAFJpxYc\nnyjp8vz4MknT646dlsdXSToyjx0g6ba61xOSPt7uPZYdkAfwI0m3SDqxqIGkEyWtkLRi/fr1JT/O\nbOyqzQI0e5lZz2iaH50bzVrTidwoaRxwPnAUMAs4TtKshmYnAI9GxH7AOcBZ+bmzgD7gQGAe8FVJ\n4yJiVUQcHBEHA68Ffgt8r937LDsgPywiXkN2gydJekNjg4hYGBGzI2L25MmTS36c2dglsn+QzV5m\n1jOa5kfnRrPWdCg3zgEGImJ1RDwHLOKFi/TnAxfn75cAcyUpjy+KiGcj4h5gIL9evbnAryPiN0O6\nuTqlcnxErMv/fJjsp4LGDppZiwRMGORlZr3B+dGsM1rMjZNqv3HKX42/lZoK3Ff39do8VtgmIjYD\njwN7tnhuH3DZ0O/ueW0/1ClpZ2C7iHgyf/9W4MwynamUdU8Ux5cWh1ODsbOafYs/JBG/OBFP/Vz3\nr4n4NxPxN/91oj+nJ074YiI+NxEHmNbkWO/yLLhZ73N+rJcoEcZ3EvH/KQ7fdXNx/MnEZZqVINuS\niA8k4qk1EY8l4qkVSKnFzncn4n/84+L4pxLtAXa5PnHg8CYndb8WcuOG0apkJGkC8E7gtDLXKVNl\nZW/ge9lsPuOB70TEVWU6Y1ZltVkAM+t5zo9mHdKh3LgO2Kfu62l5rKjNWknjgd2AR1o49yjg1ohI\n1cFsSdsD8ohYDbyqzIeb2fNq6+TMrLc5P5p1Tody43JgpqQZZIPpPuC9DW36geOBG4GjgesiIiT1\nA9+RdDYwBZgJ1P/K5jhKLlcB53+zrtGhJ8k7WtYpj39C0kpJd0i6TNIOpW7UzMysRZ3Ijfma8JOB\nq4G7gMURsVLSmZLemTe7ANhT0gDwSeDU/NyVwGLgTuAq4KSI2AJbl6cdAVxR9j47sTGQmXVA2d3I\n6so6HUH20MlySf0RcWdds61lnST1kZV1OrahrNMU4BpJ+wMvAU4BZkXEM5IW5+0uKtFVMzOzlnRq\np86IWErDk3oRcXrd+03AMYlzFwALCuJPkz34WZpnyM26RAdmAYarrNN4YMd8Td1OwP1t3qKZmdmQ\nVGWPDs+Qj5ZU2dnGFU25lxydaP+aJp8x9wVl4TNP/aQ43p+4zn8n4p/Ztzj+y3uL4//zcHH80FT1\nlacScYCTmhzrXSVnAYpKM70u1SYiNkuqL+t0U8O5UyPiRklfAu4FngF+FBE/KtdNM+t+tyXityTi\nlyTiiSorN/+2OJ4IJ7dbeSQRPyURh2wFcJEjEvGbEvE7EvFNifjvE/EPJuKpVcmp/9QAH0mtnDi8\nyUndrwo7VXuG3KxLtDgLMFit1c72SXox2ez5DLKlLDtLet9wfqaZmVmNZ8jNbES1uE6uWa3V4Sjr\n9BbgnohYDyDpCuD1wLcG76qZmVk5nVpD3u08Q27WJTowC7C1rFO+UUEfL1yIVCvrBHVlnfJ4X16F\nZQbPl3W6FzhE0k75WvO5ZE+om5mZDTvPkJvZiCszC5CvCa+VdRoHXFgr6wSsiIh+srJOl+ZlnTaS\nDdrJ29XKOm3m+bJOyyQtAW7N4z8DFpboppmZ2ZBUYYbcA3KzLlGbBShjmMo6nQGcUbJrZmZmQ9aJ\n3NgLPCAfLRMSq4W2TzyGvVviOk2rXyYeAX9ncZjr3l0c35x4vP0/EtVUUk+wH7pT4sDaRDz1NP/Y\nVJV1cmbWTRLVVO55dXH858XhJxLp40WJT9UeiQOpal+pvLU4Ef9KIg7Z7gpFUikqlWdnJeJHJeKp\njdWXJOJvTsSbLja+ttnBnlSV3OgBuVmXqMosgJmZWauqkhs9IDfrIn7K2szMbFtVyI1VuEeznrAd\nMGGQl5mZWZV0KjdKmidplaQBSacWHJ8o6fL8+DJJ0+uOnZbHV0k6si6+u6Qlku6WdJekP2n3Pj1D\nbtZF/BOymZnZtsrmRknjgPPJ9mNdCyyX1B8Rd9Y1OwF4NCL2k9QHnAUcK2kWWUWyA8k2yLtG0v55\nJbJzgasi4ui83HDqSYRBOf+bdQnhGXIzM7N6HcqNc4CBiFgdEc8Bi8h2oa43H7g4f78EmJvvvzEf\nWBQRz0bEPcAAMEfSbsAbyMoJExHPRcRjbd6mZ8hHT+Ix7wPuL46vT1zmV00+4uBXFcdPuj5xQuLv\n0fi/K47/2dTi+AMfL45/+rfF8S9+KNGfTYn42CT8E7KZDYdmlTe+XRy+MdH8G8XhXd+UaD85EU+k\nOhJFvZLXSaQ5dk/EIT2HuSUR3zkRPywR3/H1xfEZiXJp+1xZHP9+4vp/mIgDsEuzgz2pQ7lxKnBf\n3ddrgdel2uT7ejxOVmNnKnBTw7lTgWfIRmfflPQqstJwH4uIp9vpoPO/WZeoym5kZmZmrWoxN06S\ntKLudeIIdG088BrgaxHxauBp4AVr04dyMTPrElWotWpmZjYULeTGDRExu8nxdcA+dV9Py2NFbdZK\nGk+2A8wjTc5dC6yNiGV5fAklBuSeITfrEp4hNzMz21aHcuNyYKakGfnDl328cBuqfuD4/P3RwHUR\nEXm8L6/CMoNs+8ObI+JB4D5JB+TnzAXupE2eITfrElXZjczMzKxVnciN+Zrwk4Gr88tdGBErJZ0J\nrIiIfrKHMy+VNABsJBu0k7dbTDbY3gyclFdYAfgo8O18kL8a+GC7ffSA3KxLVGU3MjMzs1Z1KjdG\nxFJgaUPs9Lr3m4BjEucuABYUxG8Dmi2VadmgA3JJFwJvBx6OiIPy2B7A5cB0YA3wnoh4tBMdqo4d\nhhROPjH+ZLPPSPzv3TXVflIifndx+Kl/KY6nKr8cnYjf9vXi+MHNynl+JRFv9mh9d/MMuVlv6Z38\n2KTKyjPfLI5/KdF+enF4aaI6yttSaeXIRDyVA1PVVFKVTnaZkjgAsKE4/N3niuOrE5dJVX557XsS\nBxI3N/W/iuOzE5XJXp64PAAva3awJ1UlN7ayhvwiYF5D7FTg2oiYSfYvve1F7GaW8Rpys55zEc6P\nZsOqKrlx0AF5RPyEbC1Nvfri6RcD7+pwv8wqadwgLzPrHs6PZiOjCrmx3TXke0fEA/n7B4G9O9Qf\ns+oSg/+L/N1IdMTMSnB+NOukiuTG0mUP85IwkTou6cRaofb161PbTZoZUI1pALOKaJYfnRvNhqAC\nubHdAflDkl4KkP/5cKphRCyMiNkRMXvy5NQTEGZWmYVyZmNbS/nRudGsRRXJje0uWakVT/9C/uf3\nO9ajMWdNIv5UcTjxUPU2e0TVe7rZZ08vDieKpvCy7xbHH0u0/1oifmMi/oVE/DeJ+Pap/xjAgamb\nOCR9TreryqPkZmNbF+bHm9KH7k/EL0rEDy0Ovy21UfnfJOKbEvEDEvFXJuJvTsS3T90Y2Wbnheek\nTymUXCaRKhXz8+LwvYlc95Khfi7AHzQ72JsqkhtbKXt4GXA4MEnSWuAMsm80iyWdQDacStX4MbNW\nuRC5WU9xfjQbARXJjYMOyCPiuMShuR3ui5mVfqrDzEaK86PZCKlAbvROnWbdQsCE0e6EmZlZF6lI\nbvSA3KxbiErMApiZmbWsIrmxArdo1iNqswDNXoNdQponaZWkAUkv2CFQ0kRJl+fHl0maXnfstDy+\nStKRdfHdJS2RdLekuyT9SbkbNTMza1EHciMMW35cI+l2SbdJWtH+TXqGvIPWJOKfLA7fm6gUeV/i\nMrMS8ZnpHiX/9370HYn2icfen/lxcTzxpPftq4rjfe8ujq9MtE8+YQ6kH9HvcSV+RJY0DjgfOAJY\nCyyX1B8Rd9Y1OwF4NCL2k9QHnAUcK2kW0AccCEwBrpG0f0RsAc4FroqIoyVNAHZqv5dmNvJuSR/6\nYSJ+WCKeqlDy6UT8FfsWx2+7tzje9//au/dgOerzzOPfxwKBl4u4SOaiSyQH2Y5wsriiCGfxhaAl\nCNtl2WsIImVHm1AoW4YEx+y6gI0xhUu7oTYB7F2cRDYKCgYLlfDlbFZGCwiMvTFCEmEBSZZ9SsiL\nZO7IQEghIfHuH90Do1H/5syZmTOnZ/r5VE1pztOX6QbpvL/zO91vF8dHPFOcP/St4vzUZo9jSjVB\neWciT3WlTH7GncXx7kQtfSyxm1Tdb9pl5fRmC/tXh9PHY1gfAX4nIp7v7Ag9Q25WHp33Wp0HDEfE\n9ojYC6wke4x3vfrHeq8G5ktSnq+MiD0R8QQwDMyTNAn4EHAzQETsjYhUI0wzM7Pu6k4f8q7Xx05O\nqYgH5GZlUeu12vxpZJNrT/fLX/Xdf6dy4O9YduYZRetExD7gJeD4JtvOAp4D/k7SP0n6hqQjOj9Z\nMzOzFnReG2Fs6iNkT+L935I2FXzmqPiSFbOyaK3X6vMRMXfsD+ZNh5D9kvpPImK9pK8AVwBf7OEx\nmJlZVZWzNtZ8ICJ2SXoHcLekn0TEA+3syDPkZmXR2ixAM7s48Jmu0/KscB1JhwCTgBeabLsT2BkR\n6/N8NemrSM3MzLqr89oIY1MfiYjan88C36GDS1k8IDcri86vk9sAzJY0K7/5chHZY7zr1R7rDXAe\nsC4iIs8X5XeZzyK7XfihiHgaeFJS7YHW84EtmJmZ9UJ3riHven2UdISkowDySzl/F3i83dP0JStd\nk7rB9oXiOPUX6A8T+dOJfOKH0oeUvJV8c3H89e3F+VcTu/lycfzriTvDN6cOZ10iPyuRAxyd2lmf\na+0n/UIRsU/SpcDafE/LI2KzpGuBjRExRHZz5q2ShoEXyXsa5OutIhts7wMuqbuD/E+A2/JvYttJ\n/y01s1I6Jr3ofS8X568Ux68mfhl/xD8m9j890U3lJ4n1FxfHrya6dB10FXBNqmMKHDjXWS9Vl9/+\nG4kFiSHUA4luKqmmeP86kb+YyN+dyJvurM91UBthbOqjpBOA72T3fXIIcHtE3NXuMXpAblYWrV0n\n11RErAHWNGRX171/DTg/se1SYGlB/ggwHtfmmZlZ1XWhNkL362NEbKeLPwF5QG5WFrXr5MzMzCxT\nkdroAblZWXRpFsDMzGxgVKQ2ekBuViYVmAUwMzMblQrURg/IzcribVRiFsDMzKxlFamNHpB3TaKb\nCg8WxycluqNcl7iF/cLU557R5JiOT+TnFMcX31mc/9azxXmq+d3MRL4/kX84kc/4s8QCgPc0WdbH\n3IjUzLrugvSiKf+tOL+tOD7i8sR+LkrkdyTy0xL5exP5zESe6OrFjHckFkC6fiTame19tDh/JLGb\n9Yn804n84UT+64l84l8mFkDWInsAVaA2ekBuVhYCJo73QZiZmZVIRWqjB+RmZSEqMQtgZmbWsorU\nRg/IzcqiIrMAZmZmLatIbfSA3KxMKjALYGZmNioVqI0ekJuVRUV6rZqZmbWsIrWxAj9zmPWJ2tPI\nmr3MzMyqpEu1UdICSdskDUu6omD5YZLuyJevlzSzbtmVeb5N0jkN202Q9E+S/qG9E8yMOEMuaTnw\nMeDZiHhvnl0DXAw8l692VUSs6eRAymdHIl+RyPck8iMT+QnF8emJ1Wf8YWLBryRygF8m8h3F8XcT\n7Q23JXZzSpOPLvLBRP6OKxMLljTZ2TGj/PA+UJFZALNB0T/1MdHqFuDXvlcYv7z0p4X50YnvUbtf\nL87v+V/F+fn3JI7nI4n8dxL5gtTFxX+cyAFOTeT3FseHFv+34J2J3RyRyNeOcv2J5ycWzE/kA6oL\ntVHSBOAm4GxgJ7BB0lBE1DdwvgjYHRGnSFoEXAdcIGkOsIjsL87JwD2S3hURtWbOlwFbgaM7OcZW\nZshvARYU5DdExGn5a8AG42bjxDPkZv3kFlwfzcZe57VxHjAcEdsjYi+wEljYsM5C3pp1XQ3Ml6Q8\nXxkReyLiCWA43x+SpgEfBb7R9rnlRhyQR8QDwIudfpCZjaA2C9DsZWal4fpo1gOt1cbJkjbWvRp/\nxT4VeLLu6515VrhOROwDXiJ7wmKzbW8EvgC80f4JZjq5hvxSSY9KWi7p2NRKkpbU/gM999xzqdXM\nzNeQmw2KEeuja6NZi1qrjc9HxNy617IxPyypdrnapm7sr90B+V8Dv0r28NungL9KrRgRy2r/gaZM\nmdLmx5lVgGfIzQZBS/XRtdGsRd2pjbuA6XVfT8uzwnUkHQJMAl5osu0ZwMcl7SC7BOYsSd9s+bwa\ntDUgj4hnImJ/RLwBfJ38Whoz64BnyM36nuujWZd1pzZuAGZLmiVpItlNmkMN6wwBi/P35wHrIiLy\nfFHehWUWMBt4KCKujIhpETEz39+6iPh0u6fZVh9ySSdFxFP5l58EHm/3AMorcTv05muK8+nFMT9M\n5B9NtC4580PF+e6/K86PbXL3PP83ka8vjhtvb6jRJxMLbk/kh6cPydLcZcWs75WzPp7RZNlthenR\nW36rePUvF8fHJpp6nZ/qRPJqIp+ZyFNdujgtkb+W2oDsEuAiibqc6CDDLxL5M4n83/9GYsHnE/mn\nEnmqe9uA6kJtjIh9ki4lG9xNAJZHxGZJ1wIbI2IIuBm4VdIw2b0hi/JtN0taBWwB9gGX1HVY6ZpW\n2h5+CziT7IL5ncCXgDMlnQYEWQ+9Zv2FzKxVngU36xuuj2Y90oXamHc8WtOQXV33/jWgsNdkRCwF\nljbZ9/3A/Z0c34gD8oi4sCC+uZMPNbMCniE36yuuj2Y9UJHa2NYlK2Y2BoSfnWtmZlavIrXRA3Kz\nshCQeuicmZlZFVWkNnpAblYmFZgFMDMzG5UK1EYPyJOeLo5Td4YffW5x/sz3Exskbkm/5dHi/N8k\ndpN8JBOk7p7n9peL8+MSu1mQ6nnvbipd1YXr5CQtAL5CdgvMNyLiLxqWHwb8PfCbZP1VL4iIHfmy\nK4GLgP3An0bE2rrtJgAbgV0R8bHOjtLMeqvZ9+q5xfGv/U1x/jf/oTj/o8Tuf5DIU89CeqU4fvma\n4vzo7Q8VL5g1KfEBAP+nON76L8V54/Mca7Yn8lS95vpEfnoir1g3lZSKXENegZ85zPpEh71W80Hz\nTcC5wBzgQklzGla7CNgdEacANwDX5dvOIWvxdCqwAPhavr+ay4Ct7Z+cmZlZGyryjA4PyM3KovOn\nkc0DhiNie0TsJXtyWGN3+YXAivz9amC+JOX5yojYExFPAMP5/pA0Dfgo8I1OTs/MzGzUKvIUaw/I\nzcqitVmAyZI21r2W1O1hKvBk3dc7OfiXrW+uExH7gJeA40fY9kbgC8AbnZ2gmZnZKFVkhtzXkJuV\nRWvXyT0fEYmLPrtP0seAZyNik6Qze/W5ZmZmgK8hN7Nx0NkswC5get3X0/KscB1JhwCTyG7uTG17\nBvBxSTvILoE5S9I3R3dSZmZmHfAMeZX9vDh+sjhmXqITyVcT66fuSH+9ySEVGk4v+vNEN5UNifXP\nS+3Id3r3ROezABuA2ZJmkQ2mFwG/37DOELAY+DHZ//F1ERGShoDbJV0PnAzMBh6KiB8DVwLkM+T/\nMSI+3dFRmlkfWFwcH/3h4nz19xL7WV4c3/7T4nx94mNTXcBmzSjON92d2AD4USL/7USeqvuf+FeJ\nBanCPz95SNZEl2bIu92FTNLhwAPAYWTj6dUR8aV2j88DcrOyqF0n16aI2CfpUmBtvqflEbFZ0rXA\nxogYInus962ShoEXyQbt5OutArYA+4BLImJ/J6djZmbWsQ5rIxzQhexssnukNkgaiogtdau92YVM\n0iKyLmQXNHQhOxm4R9K7gD3AWRHxz5IOBX4k6fsR8WA7x+gBuVlZdGEWICLWAGsasqvr3r8GnJ/Y\ndimwtMm+7wfu7+wIzczMRqE7M+RvdiEDkFTrQlY/IF8IXJO/Xw38j8YuZMAT+YTWvPw3yP+cr1/r\n9xLtHqCvITcrkwpcJ2dmZjYqnXUggzHqQiZpgqRHgGeBuyMicdHVyDxDblYWFbmT3MzMrGUl7EBW\nk1/aeZqkY4DvSHpvRDzezr48Q25WFiL7F9nsZWZmViXdqY1j0YXsTRHxS+A+siddt8Uz5EmvFccz\nU+ufXhyf/D9H97GpZyGuTuRL/jy5q7sSVwMv+NPifEfjL3hyMy/2X5OeEDBxvA/CzAzg8ET+nkQ+\nM5GfUhz/fqIQTfpFcX52Yvff/X/F+T8m1gd4ZyK/L5GnZmdPbXwQcs0ZTT7cRq07tbHrXcgkTQFe\nj4hfSno72d/S69o9QI+0zMrCl6yYmZkdqDsND7rehUzSScCKvIPL24BVEfEP7R6jB+RmZdGF1k5m\nZmYDpUu1sdtdyCLiUeB9nR9ZxgNys7LwDLmZmdmBKlIbPSA3KxPfuGlmZnagCtRGD8jNysI3dZqZ\nmR2oIrVxxAG5pOnA3wMnkD2BaFlEfEXSccAdZLdW7wB+LyJ2j92hlsTrqQUvFMfTi2NOLL4z/P5n\nilc/M44rzHfpxdQB8alE/urPivOjknv6L8kl1kW11k5mVnrVrY2JDmTJ5hKriuP/nuim8u8Su0l1\nGks9huWERA7wq4n8J4k8dUx8IZEnOstYeypSG1s5xX3A5RExB3g/cImkOcAVwL0RMRu4N//azNpV\nmwVo9jKzsnBtNOuFitTGEQfkEfFURDycv38F2Er2yNCFwIp8tRXAJ8bqIM0qww8GMusLro1mPVSB\n2jiqa8glzSRr8bIeOCEinsoXPU3iF0SSlgBLAGbMmNHucZoNvorcSW42aFwbzcZQRWpjyz9XSDoS\nuBP4XES8XL8sIoLsGrqDRMSyiJgbEXOnTJnS0cGaDbRar9VmLzMrFddGszFWkdrY0gy5pEPJvuHc\nFhHfzuNnJJ0UEU/lTyt6dqwO0qwSKjILYDYoXBvNeqAitbGVLisie5zo1oi4vm7RELAY+Iv8z++N\nyRGOm8Sd5K+k1t9RHK8pjhkqjs+8JrH+WcXdVFYUpplXEzd6P/394vzE307t6TebfIp1lUb6UX9/\nTw7DzJqrbm0cTuTLi+NNxR3FOCaxmwcT+Q8SeWqg9u5EDum6fEEin/qZxIL3JnJ3lO66CtTGVi5Z\nOQP4DHCWpEfy10fIvtmcLelnwL/Nvzaztr0NOHyEl5mVhGujWU90pzZKWiBpm6RhSQd1P5J0mKQ7\n8uXr83tDasuuzPNtks7Js+mS7pO0RdJmSZd1cpYj/hgXET8i+4VBkfmdfLiZ1ROeWTHrD66NZr3S\neW2UNAG4CTgb2AlskDQUEVvqVrsI2B0Rp0haRNZc/4K8neki4FTgZOAeSe/irdanD0s6Ctgk6e6G\nfbZsQJrFmA0C4RlyMzOzel2pjfOA4YjYHhF7gZVkLUrr1bcsXQ3Mzy9NWwisjIg9EfEE2XVb85q0\nPm2Lp+PMSsMz5GZmZgdqqTZOlrSx7utlEbGs7uupwJN1X+8ETm/Yx5vrRMQ+SS8Bx+f5gw3bHjDw\nbmh92hZXf7PSEHDYeB+EmZlZibRUG5+PiLk9OJiDNGt9OhoekCelbgFPiF+Mbv3tiTxxZ/gPbyzO\nr7q8yWf8bXF84rLinIvPTyw4scmHWPd4htzMym5tcXxXoptK6mqC6Yk81U3l5ESeaIjGnkQOMDuR\nv/+LiQWfTeT+ft0bXamNuzjwb920PCtaZ6ekQ4BJwAvNtk20Pm2LryE3Kw13WTEzMztQV2rjBmC2\npFmSJpLdpNnYgLrWshTgPGBd/nCvIWBR3oVlFtmPdA81aX3aFg/IzUqjNgvQ7DXCHkre1snMzGx0\nOq+NEbEPuJTsVzxbgVURsVnStZI+nq92M3C8pGHg88AV+babgVXAFuAu4JKI2E+69Wlb/PsWs9Ko\n3Une5tZ90NbJzMxsdDqrjTURsYaGx0JFxNV1718DCq/djYilwNLIDHcLAAAJg0lEQVSGrFnr01Hz\nDLlZaXQ8C1D6tk5mZmaj0/kMeT8YjLMwGwi16+SaatbaqfRtnczMzEanpdrY9zwgT0q0O3k4sfq0\nRP56Iv9mIn+uOP7g7ybWTx0PpO8kv/jcxIKvNtmZ9caI/yTHpbVTt9o6mVm/21Ycp2pd6rvVf07k\nexP5BxP5LxP5Y4kc4M9SC/4gkbvT2Pgb/OHq4J+hWd/oeBag9G2dzMzMRqcaM+S+htysNDq+Tq70\nbZ3MzMxGx9eQm1lPdTYLkF8TXmvrNAFYXmvrBGyMiCGywfWteVunF8kG7eTr1do67SNv6yTpA2Rt\nnR6T9Ej+UVfld6ubmZmNsWrMkHtAblYqEzrauuxtnczMzEavs9rYDzwgNyuNaswCmJmZta4atdED\ncrPSqF0nZ2ZmZplq1MbBP8O2zS+OUy2cfp7IL0jkqxP5LYl8bSI/IZED/Kd3JBYsT+Ru7TS+uvM0\nMjOzsZNoe/hKYvV/SeTrEvlRifxjiTzV9vC/HpdYANlT0IvMbLKNjZ9q1EZ3WTErjWrcSW5mZta6\n7tRGSQskbZM0LOmKguWHSbojX74+fxhebdmVeb5N0jl1+XJJz0p6vIMTBDwgNyuR2nVyzV5mZmZV\n0nltlDQBuAk4F5gDXChpTsNqFwG7I+IU4AbgunzbOWQdyU4FFgBfy/cH2XUNC9o/t7d4QG5WKp4h\nNzMzO1DHtXEeMBwR2yNiL7ASWNiwzkJgRf5+NTA/fxbHQmBlROyJiCeA4Xx/RMQDZC2EO+YKb1Ya\n1biT3MzMrHVdqY1TgSfrvt4JnJ5aJ3+ux0vA8Xn+YMO2Uzs9oEYjzpBLmi7pPklbJG2WdFmeXyNp\nl6RH8tdHun1wZtXia8jN+oVro1mvtFQbJ0vaWPdaMl5H265WKvw+4PKIeFjSUcAmSXfny26IiL8c\nu8MbTzOL4/nvLM4f2F6cfyCx+3cn8h8m8sTH8tl5iQUAf5vI3U2lnKpxJ7nZgKhobUwUr08+UJx/\nNrGbpxP5jaPMv5DIuT61APhwIvekRzm1VBufj4hUHzyAXcD0uq+n5VnROjslHQJMAl5ocduOjThD\nHhFPRcTD+ftXgK2MwVS9mXmG3KxfuDaa9UpXauMGYLakWZImkt2kOdSwzhCwOH9/HrAuIiLPF+Vd\nWGYBs4GHOjqlAqO6qTNvAfM+YH0eXSrp0bzty7FdPjazinkbcNgILzMrG9dGs7HUeW2MiH3ApWRP\nddkKrIqIzZKulfTxfLWbgeMlDQOfB67It91M1rx+C3AXcElE7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f14a5b5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "for i in range(5):\n",
    "    plt.subplot(5, 2, 2 * i + 1)\n",
    "    plt.imshow(np.reshape(imgs[2 * i], [28, 28]), cmap='hot_r')\n",
    "    plt.title('Digit: {}'.format(2 * i))\n",
    "    plt.colorbar()\n",
    "    \n",
    "    plt.subplot(5, 2, 2 * i + 2)\n",
    "    plt.imshow(np.reshape(imgs[2 * i + 1], [28, 28]), cmap='hot_r')\n",
    "    plt.title('Digit: {}'.format(2 * i + 1))\n",
    "    plt.colorbar()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
